SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION

Bachelor Degree Project in Production Engineering G2E, 30 credits Spring term 2020

Authors: Fernando Martínez Gil Mario Martínez Gil

Supervisor Jernbro: Carolin Ryberg Persson Supervisors University of Skövde: Martin Birtic Kaveh Amouzgar

Examiner: Amos Ng

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil Abstract

Simulation is becoming more important nowadays, where there is a need for developing or improving projects more efficiently without taking any economic risk. Jernbro Industrial Services AB in Skövde is specialized in Automated Guided Vehicles (AGVs) production, and so far, they have been calculating the optimal number of AGVs of a system by using Excel. However, they want to go a step further and include simulation in their projects to obtain this data more accurately and efficiently and update their working procedures.

In this thesis, former articles and researches have been studied to acquire knowledge in previous works related to the calculation of the optimal number of AGVs. A market survey has been conducted to find the best software simulation tool in the market for AGVs, by analysing the main features of each software. Although many software satisfies the requirements, FlexSim is the tool that has been chosen, as it provides good results in a user-friendly way. Therefore, in order to validate whether simulation provides accurate and interesting data for the company, an already existing model has been simulated to compare the new results with the former ones calculated with Excel. After discussing the results and the issues that have been confronted during the project, it has been found that the optimal number of AGVs in the system is 3 but, in view of security and the unpredictable behaviour of AGVs systems, 4 is the number considered as the best solution for the model. Moreover, all the benefits of simulation are presented as a solution for future projects the company develops.

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil Certification

This thesis has been submitted by Mario Martínez Gil and Fernando Martínez Gil to the University of Skövde as a requirement for the degree of Bachelor of Science in Production Engineering. The undersigned certifies that all the material in this thesis that is not my own has been properly acknowledged using accepted referencing practices and, further, that the thesis includes no material for which I have previously received academic credit.

Fernando Martínez Gil Mario Martínez Gil

Skövde 2020-05-18 Institutionen för Ingenjörsvetenskap/School of Engineering Science

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SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil Acknowledgements

In this section, we would like to thank all the people that helped us develop this project, both academic and personal field.

First of all, we would like to thank all the Jernbro team for giving us the opportunity to take part in this company and for making us feel very comfortable during our stay. Thank you to our supervisors Carolin Ryberg and Martin Bratt for all the interesting meetings, answering our questions and giving us the chance to develop this project. We have enjoyed this experience. Thank you to Peter, Pär and Johan as well for all the support regarding technical aspects and the interesting conversations.

We would like to thank the University of Skövde for the academic experience. Especially, our supervisor Martin Birtic for guiding us throughout the project, supporting us and having a good time talking.

Thanks to FlexSim team for providing us with the full license of the software and helping us with every question we had regarding the functioning of the software.

We do not want to forget to thank our Erasmus friends we met this year for their support and the countless hours we spent doing our projects together.

Finally, we would like to thank our family for their daily support from the distance, and especially to our parents. Without them, we could not have lived this incredible experience in Sweden.

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil Table of contents

Abstract ...... ii Certification ...... iii Acknowledgements ...... iv Table of contents ...... v List of Figures ...... vii List of Tables ...... viii 1. Introduction ...... 1 Jernbro Industrial Services AB ...... 1 Problem description ...... 1 Aim and objectives ...... 2 Delimitations ...... 2 Sustainability ...... 3 2. Theoretical Frame of Reference ...... 4 Industrial Systems ...... 4 The problem of material handling ...... 5 AGVs ...... 6 Industry 4.0 ...... 7 Simulation ...... 8 2.5.1 System ...... 8 2.5.2 Advantages and disadvantages ...... 8 2.5.3 Steps to achieve a good simulation ...... 9 2.5.4 Discrete Event Simulation ...... 10 3. Literature Review ...... 12 AGVs optimal number ...... 12 3.1.1 AGVs optimal number in Flexible Manufacturing Systems ...... 12 3.1.2 AGVs optimal number in Port Container Terminals ...... 13 3.1.3 AGVs optimal number with a queuing system ...... 14 Conclusion ...... 14 4. Method ...... 15 Problem understanding ...... 16 Software research ...... 16 Simulation process and results ...... 16 5. Problem formulation ...... 18 6. Software survey ...... 20 Why is a survey needed? ...... 20 Jernbro’s requirements ...... 20 Software comparison ...... 21 Selection of the final software ...... 21 7. Case of study ...... 23 Layout ...... 23 Flow Description ...... 24 AGV Logic ...... 24

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil

Charging stations ...... 25 Data Collection ...... 25 8. Development ...... 28 Assumptions ...... 28 Model translation with FlexSim ...... 29 8.2.1 Creating the AGV system ...... 29 8.2.2 Creating the workflow logic ...... 32 8.2.3 Issues while developing the model ...... 33 9. Results ...... 34 Validation of the model ...... 34 The optimal number of AGVs ...... 35 10. Discussions ...... 39 11. Conclusions ...... 41 12. Future Work ...... 42 List of references ...... 43 Appendices ...... 46 Appendix 1: Work Breakdown and Time Plan ...... 46 Appendix 2: Simulation Software Comparison ...... 47 Deep description of the four-finalist software ...... 49 Arena ...... 49 Anylogic ...... 49 Tecnomatix Plant Simulation ...... 50 FlexSim ...... 50 Appendix 3: AGV charging logic ...... 52 Appendix 4: AGV Utilization Data ...... 53 Appendix 5: Simulation model video example ...... 54

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil List of Figures

Figure 1. The effect of AGVs number variation on productivity (Fethi & Mehdi, 2019) ...... 13 Figure 2. The effect of AGVs number variation on the total time spent per AGV (Fethi & Mehdi, 2019) ...... 13 Figure 3. Methodology flowchart ...... 15 Figure 4: AGV Path and machinery display ...... 23 Figure 5: Flow description ...... 24 Figure 6: Charging stations ...... 25 Figure 7: AGV TC55 3D Model ...... 26 Figure 8: Different path elements ...... 30 Figure 9: Representation of TC55 using the Task Executer 3D object from FlexSim ...... 30 Figure 10: Standard operation station ...... 31 Figure 11: Entries and exits of the system ...... 31 Figure 12: AGV model in FlexSim ...... 32 Figure 13: Deadlock example ...... 33 Figure 14: AGV Utilization data for 4 AGVs ...... 35 Figure 15: Exit throughput according to the number of AGVs ...... 35 Figure 16: Assignments per hour and number of AGVs ...... 36 Figure 17: Total WIP after running the model with 2 AGVs ...... 37 Figure 18: AGV Utilization data depending on the number of AGVs ...... 38 Figure 19: Time since a fixture is ready until it arrives at the next station ...... 38 Figure 20: Updated Timeplan ...... 46 Figure 21: Original Timeplan ...... 46 Figure 22: Recharging logic ...... 52 Figure 23: AGV utilization data in scenarios from 2 to 5 AGVs ...... 53

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SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil List of Tables

Table 1: MoSCoW method for Jernbro requirements ...... 20 Table 2: Pugh chart for software election ...... 21 Table 3: AGV TC55 components ...... 26 Table 4: AGV TC55 main features ...... 27 Table 5: Assignments per hour for every process ...... 27 Table 6: Assumptions for model developing ...... 28 Table 7: Assignments per hour after running the model for a week ...... 34 Table 8: Software Comparison I ...... 47 Table 9: Software Comparison II ...... 48

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SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil

1. Introduction

This chapter provides an overview of the project. First, the reader will find a description of Jernbro, the company in charge of this project. Next, the problem description and the aims and objectives are specified. Finally, the delimitations and sustainability conclude this chapter.

Jernbro Industrial Services AB

The company is one of Sweden’s leading players in industrial services with approximately 950 employees and a turnover of 1400M SEK. Its activity, as it is currently known, started in 2005 when they acquired AB Volvo’s service company Celero, focussed on the automotive industry. They are in 26 locations all around Sweden and their employees encompass mechanics, electricians and engineers among others. Some of their customers are Volvo, Sandvik, Scania, ABB or Siemens.

The company offers services in order to improve the maintenance and productivity of Swedish local companies. Their strategy is to stay closer to the customer. That means they can provide quality services by establishing closer relationships with the clients and providing all the material in a short time. They have experience in the fields of metal, plastic & rubber, wood, automation, food, steel, pulp & paper, chemicals and energy industry.

In Skövde, they are specialized in the production and selling of Automated Guided Vehicles (AGVs) for industrial use. They provide with custom-made products for a wide range of companies all over the world.

Problem description

Automated Guided Vehicles are complex systems that autonomously transport goods inside factories or logistic centres. When selling new AGV systems there is often a need to do a capacity calculation to get an idea of how many AGVs there need to be in the system to cover the customer’s needs. Jernbro wants to explore more efficient and accurate ways to carry out these estimations rather than the current method based on mathematical calculations through Excel. In addition, Jernbro wishes to have more and better data, including visualizations, before the AGV system is sold.

For this thesis, a real project from a Swedish manufacturing company will be used. The company wanted to introduce AGVs for material handling between different ovens and conveyors around the facility. Jernbro provided with the current layout and all the data from this real case of study. By using simulation, they want to predict the optimal number of AGVs before the real system is built. Comparing the new data with the one they already have will help make conclusions about the validation of the simulated model. This tool will make the company more competitive in the market of AGVs as they will have previous information about the system to show to their clients, and a proper estimation of the number of AGVs they have to sell will save costs to the company and make their systems more efficient.

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil

Aim and objectives

The aim of the project is to use a market study to identify a suitable simulation software for AGV systems in order to build a simulation model based on a real-world case study. The created simulation model should, in turn, be used to predict the optimal number of AGVs for the system and to compare the result with a manual prediction calculation.

Therefore, the objectives of this project are:

• Research of previous work related to calculating the optimal number of AGVs in an AGV system.

• Perform a market study to find the best tool according to several specifications to simulate an AGV system.

• Understand the real case of study and all its specifications to simulate the system properly.

• Develop the system in the chosen simulation tool and obtain data.

• Understand the data obtained and make conclusions about the results against the previous calculations from the company.

Delimitations

There will be some boundaries that will restrain some aspects of the project:

• A real case of an existing production process is simulated. This means no changes can be done on the layout or any other part of the process.

• Only the AGV layout will be simulated. The connections between the AGVs and the rest of the process will be simulated as inputs and outputs of the process.

• Due to the complexity of the built-in AGV logic in the real system, some assumptions will be done.

• Existing software will be used to simulate the AGV system. Therefore, only the features included in that software will be used for the simulation.

• The software cannot provide with the exact number of AGVs needed just by introducing the layout. The proper number of AGVs will be obtained by analysing the data collected when trying different scenarios.

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil

Sustainability

“Sustainable development is a development that meets the needs of the present without compromising the ability of future generations to meet their own needs” (WCED, 1987)

This definition of sustainability is part of the so-called report Our Common Future, developed in 1987. The U. N. General Assembly created this commission to propose solutions to an environmental problem that was affecting the world and needed to be solved by the beginning of the new century. It was not the first time this problem was addressed in deep - previous conferences took place in Stockholm in 1972 (Mebratu, 1998) - but the WCED set a starting point where people and companies began to concern about this environmental issue and started to take actions to solve it, although it was sometimes challenging to find solutions to all these problems.

Jernbro has implemented different sustainability measures within economic, social and environmental fields. The company applies sound business ethics to ensure profitable and stable development to all the members involved in a project. They stimulate their employees as a way of establishing a comfortable working atmosphere, which is important to achieve efficiency and optimal results. The company has implemented policies to reduce their environmental impact and influenced its suppliers to behave in the same way. When they sign a contract, they make sure that all parts related to the project fulfill these sustainability requirements. Jernbro is environmentally certified according to ISO 14001 and audited by Det Norske Veritas in Sweden (Jernbro , 2019).

With this project, Jernbro wants to optimize to the fullest the number of AGVs they sell to their clients. They will estimate the proper number by using simulation. This will save costs to the company as no more AGVs than needed will be sold to the customer. Simulation will help to find the optimal layout, which means no later changes have to be done in the future, and, as a consequence, more saving costs for the company. Therefore, they will save energy due to transportation optimization. Finally, they can simulate different scenarios and check between different alternatives to see which one wastes fewer resources and choose the most economical and environmentally friendly solution.

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil 2. Theoretical Frame of Reference

This chapter is about theoretical definitions that might help understand further concepts related to AGV simulation. Industrial systems and the problem of material handling gives a general knowledge of the main topic. AGVs are shown as a solution to solve this issue. Industry 4.0 and different concepts within simulation conclude this chapter. The information used to develop the theoretical frame of reference comes from scientific articles, websites and other final year projects from the University of Skövde.

Industrial Systems

According to EAE Business School (2018), an industrial system is an organized set of processes where the combination of different factors of production (technology, information, talent, teams, capital and raw material) creates useful goods that will be commercialized once they have been manufactured. This is the foundation for every process within the manufacturing industry.

The idea is simple: there are inputs, e.g., raw materials, that need to be manufactured in order to get the desired product that will be sold into the market. The required processes to achieve this task and everything involved directly or indirectly within the process are considered part of the industrial system. This whole cycle can be analysed with a view to optimize and transform the different processes to achieve more efficient and quality systems.

A production system might be characterized by both physical movements of materials and information flows. These channels of movement along the process will set the constraints of the capacity of the production system and will limit the outputs (Holstain & Tanenbaum, 2012). Quality is another limiting factor, as long as it will determine the manufacturing process of the product to achieve the client’s expectations.

Three sorts of industrial processes can be found:

• Project system: only one product is built at a time, usually for big products such as ships or airplane prototypes.

• Batch system: small quantities of identical products. Different models can be built by easily changing the machines.

• Continuous system: big quantities of identical products. Assembly lines are used in this process and there is usually a high level of automation.

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil

The problem of material handling

“Material handling is a process that includes short distance movements inside the scope of a building, or between the transportation vehicle and the building” (Massey, 2017).

It is important to differentiate between external logistics, which encompasses transportation of raw material from the supplier to the plant and finished goods from the plant to the final destination, and it is carried out by trucks, trains, ships and other big means of transport; and internal logistics, where material handling is the procedure applied to move material around the facility (Groover, 2015).

Material handling is an unavoidable process in the manufacturing industry. It is essential for transporting material all around the process. Moreover, it helps productivity and increases the industry’s profitability. Nevertheless, it is not a production process itself; therefore, it doesn’t add value to the final product. For this reason, it should be almost eliminated or make it as simple and cheap as possible. An inefficient material handling system might cause the company to lose money and go bankrupt. It is a challenge to create an optimal, simple and not expensive system to move material around the factory (Ray, 2008).

As pointed by Ray (2008), to achieve a good material handling system, these requirements must be fulfilled: a safe and efficient movement to the requested destination, timely movement of materials when needed, a supply of material at the requested rate, storing of materials using minimum space and choosing the lowest cost solution to solve the problem.

Material handling encompasses different functions, such as conveying, elevating, positioning, transporting, packaging and storing. There are two types of systems to develop these tasks according to the level of automation involved:

• Manual Material Handling: physical force is used to move materials. Mechanical machines are utilized to perform these movements. Although it is essentially manual, some kind of man- controlled vehicles can be used, e.g., forklifts. This has been the traditional way of material transportation in the industry since it is easier and cheaper to install. However, several issues can be found. This system has a slower operation speed compared to the automated ones. Employees are needed to transport the material and operate the vehicles, which increases labour costs. As they rely on humans, error and inaccuracies are common. Sometimes, workers are exposed to difficult, repetitive and dangerous tasks that may result in harmful situations. (HHI, 2019)

• Automated Material Handling: robots, conveyor systems and other computerized devices are involved. No human aid is needed unless for specific situations. Most of the problems within manual material handling are solved by using automation. This system is more accurate, faster, more agile and easily scalable. It increases productivity and might control the production according to market demand. Automated systems can help optimize storage space. They are suitable for repetitive and dangerous tasks. On the other hand, these systems are difficult to implement. They are expensive and usually take years to fully implement them, as well as the technical knowledge involved in the process. (HHI, 2019)

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil

As a conclusion, more and more companies are automating their material handling systems in order to solve all the problems that entail the manual one. It seems that automation is the future within this field, but it is not always the right solution. It will depend on the company’s needs to figure out whether a manual or automated material handling system should be implemented or not. Things such as budget or volume of production will play an important role in the final decision.

AGVs

The company, which project is going to be developed in this thesis, chose an automated material handling system to carry out the material transportation.

AGV, which means Automated Guided Vehicle, is a mobile robot that follows a guided path to transport materials around a manufacturing facility or a warehouse (Fethi & Mehdi, 2019). It does not require a driver to perform its mission. The AGV goes behind a sequence with a view to handle the material around the different stations and what to do when it comes to one of these stations. According to Chihani (2018), an AGV must fulfill five features: self-driving, collision warning, navigation facilities, path selection and destination selection.

AGVs can interact with the environment and receive information from other devices, or work as an individual fleet with no contact with other automated systems inside the facility. Our case of study is the first situation. The AGVs receive orders from the master, which is the brain of the system, and It is connected to all automated machines and devices inside the factory. This master sends guidelines to the AGVs when a task is required.

In order to operate an AGV it is necessary to consider three functions (Fethi & Mehdi, 2019):

• Dispatching: tasks are assigned to the vehicle. • Routing: the vehicle chooses between different paths. • Scheduling: arrival and departure times of vehicles are set for each section as well as possible collisions between paths.

AGV’s movement can be performed in several ways depending on the technology used. Groover (2015) and Chihani (2018) mention several techniques:

• Embedded guided wires: the wires are buried underground. The AGV follows the wire path by using frequency sensors. Previous planning is crucial to avoid future changes, as burying the wires is a tedious and expensive task.

• Paint stripes: optical sensors are used to follow the path. Suitable for places with a lot of electrical noise.

• Magnetic tape: same technology as embedded guided wires is used, but the tape is now above the floor’s surface. It gives more flexibility to the path as it is easier to make changes on the layout.

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil

• Laser guidance: The AGV’s master computer stores a mapping of the whole facility. There are several fixed reflectors all over the place, which are used as reference points. The AGV continuously sends a laser beam to detect the reflectors and set the exact position of the AGV inside the facility.

• Inertial navigation: A gyroscope is installed in the AGV and some magnetic spots are placed along with the facility. There is an onboard computer to set the position of the AGV at any moment by calculating the variation of speed and acceleration.

AGVs are used in several fields within the manufacturing industry. According to the AGV’s application report from MHI (2012), they are suitable for multiples tasks, such as repetitive movements of materials, regular delivery of stable loads, tracking material processes and dangerous or difficult jobs. They are useful for assembly lines, where a constant speed is needed; moving raw and finish goods around the facility; or pallet handling. Other applications can be found outside this field: from container handling at maritime container terminals to move material in hospitals or transporting people in amusement park rides. Different shapes and sizes are needed to develop all these tasks. Jernbro encompasses almost every application within the manufacturing field. They have all kind of vehicles, from simple and small sizes to big, complex models.

The usage of AGVs has increased in the last years as a consequence of its good performance and the resulting cost savings for the company. Material handling is an indispensable task in the manufacturing process. AGVs can perform the same tasks as traditional transporting but saving time, improving the security of the process and hiring fewer employees.

Safety is very important when it comes to AGVs, as they are driverless vehicles and most of the time they are not supervised. According to Groover (2015), an AGV cannot have a higher velocity than the average walking speed of a person. They have sensors to avoid collisions and stop when someone crosses or something falls in the AGV’s safety zone. Finally, they need bumpers to stop the vehicle when needed and light warning to alert about any possible issue.

Industry 4.0

The first industrial revolution came in the XVIII century with the invention of the steam machine and the mechanization of processes. After that, two major events changed the principles of the industry one more time: the development of the electricity for mass production and assembly lines and the automation of industrial processes.

Industry 4.0 is the fourth revolution occurred in the manufacturing industry. Also called the Internet Industry or “the second machine age”, is a new era where computerization from Industry 3.0 is optimized. The concept of “smart factories” arises. Virtual and physical systems of manufacturing cooperate worldwide with each other in a flexible way (Schwab, 2016). Computers, machines, storage systems and devices are connected through a smart network where big loads of information are transferred from one spot to another. Therefore, results can be obtained by analysing this data in order to enhance productivity, optimize efficiency, and thereby profits. (Gilchrist, 2016).

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil

The emerging technologies and the continuous signs of progress in different fields such as the internet of things (IoT), artificial intelligence (AI), robotics, autonomous vehicles, nanotechnology, biotechnology or quantum computing are taking part in this revolution, still in growth.

Simulation

“Simulation is the imitation of the operation of a real-world process or system over time” (Banks, et al., 2010).

Simulation arises as a necessity to be able to predict and study a system’s bearing before the real system is built. Testing the real system is often a difficult, tedious and inefficient task which ends up being a waste of money and time. Furthermore, no useful or desired results are obtained. That is the reason why simulation has become a valuable tool within the engineering field in terms of developing any kind of manufacturing project. As pointed by Seila (1995), simulation is used to approximate the real system; it is not an identical copy of the process. The objective is to test the system applying different scenarios and inputs to analyse its behaviour and collect as much data as possible before the real system is built.

Simulation has a wide range of applications apart from analysing manufacturing systems: designing and operating transportation systems, determining hardware and software requirements, evaluating designs for service organizations such as hospitals or fast-food restaurants, redesign of business processes and a vast list of applications (Law, 2007).

2.5.1 System

“A system is the collection of entities, e.g., people or machine, that act and interact together toward the accomplishment of some logical end” (Law, 2007). It is the part of the process considered the subject of study, so it can encompass from a step of a process to the whole process itself.

According to Law (2007), the project objectives will determine the boundaries of a system. Depending on the case, a system can vary from a whole production line to a small sub-operation within the production process. Nevertheless, it is important to consider that the model will always have both assumptions and limitations; therefore, the model will have everything needed for a proper and accurate simulation, but it will not be too complex, so its development is plausible.

Systems might be split into two categories: continuous and discrete systems. The first one is considered when the state variables change gradually over time, meanwhile, the second one is used when the state variables change its value immediately in a specific period. Simulation-based on discrete systems will be used within this thesis.

2.5.2 Advantages and disadvantages

It is important to know whether simulation is worth it or not. Building a real system without making any tests in advance is usually a tedious and risky task. However, sometimes it is cheaper and easy to build the real model rather than simulate it due to the high cost of simulation tools. In order to clarify

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil this aspect, a list of advantages and drawbacks within simulation has been established based on the researches made by Banks (2010) and Shannon (1998) during the Winter Simulation Conference:

Advantages:

• Test the system in different ways without compromising the real system.

• Understand why the system behaves in that way by examining every part.

• Diagnose and identify problems.

• Predict results.

• Prepare for changes by posing several what-if questions.

• Build consensus: the information obtained from a simulation helps planning from an objective perspective instead of trusting one person’s opinion.

• Usually cheaper than building the real model.

Disadvantages:

• Special training for model building and software testing.

• Simulation results might be difficult to interpret due to randomness inputs.

• Simulation can be time-consuming and expensive to develop.

• Simulation tools have a high-level cost.

• The model is too complicated to simulate it.

Therefore, depending on each situation, the user must decide when simulating is an affordable solution to its issue.

2.5.3 Steps to achieve a good simulation

When it comes to simulating, it is difficult to consider every step within the process since the very beginning. Create a good simulation model is the key to achieve good results, and consequently, build the real system based on the previous results obtained by using simulation. Law (2007), sets ten steps to accomplish the simulation process in a simple, organized way. It must be considered that despite the process has a sequential nature, there will be steps where some features must be appraised one more time, and it will be necessary to return to previous steps to solve the problem. Thus, some iterative processes will appear while simulating a system:

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil

1. Problem formulation and study the plan: This is the step where the problem is stated properly. The overall objectives of the study should be specified. It is important to establish the accuracy of the model as well as the range of the study. Timeframe, resources and software tools must be settled.

2. Data collection and model definition: Specify operating procedures and system structure. Data must be collected to indicate the model parameters. The model must begin as a simple concept, and further features will be added when necessary. Try to avoid complex models. They might consume a lot of time and might be difficult to extract understandable data.

3. Validation of the assumptions document: Qualify members of the team within this field must approve the model specifications to ensure the quality of the project and proper development of future steps.

4. Build a computer program and verify: Use a simulation software or a programming language tool to build the model. Simulation software packages are usually more expensive to obtain, but they don’t require deep insight within programming, which means less time and lower costs to the project

5. Make pilot runs: Test your program to ensure the good quality of itself. Collect data for future validation.

6. Validation of the programmed model: Compare the model with an existing system if possible. The experts within the work field must review the model to ensure its correctness. Perform a sensitivity analysis to acknowledge the most relevant parts of the model that affect the performance measures; hence those parts are modelled precisely.

7. Design experiments: Decide the length and the warmup period for each run, as well as the number of replications.

8. Make production runs: Carry out several production runs and collect data.

9. Analyse output data: Determine the overall and specific performance of several system configurations and compare the results between these configurations.

10. Document the results: Everything done within the project must be documented: assumptions, software used, results, conclusions. Using animated software simulation might help understand the behaviour of the system. Finally, discuss the results and validation process to ensure the quality of the simulation.

2.5.4 Discrete Event Simulation

Also known by its abbreviation (DES), is a method in which the state of a model changes at only a discrete, but possibly random, set of time points (Zhang, 2010). Some concepts are defined to get an overview of DES:

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil

• Events: an instantaneous phenomenon that changes the state of a system. • Entities: the unit of transaction. This unit of work moves between one state to another. • Attributes: information about the entity.

DES models several events that take place along time. It is assumed no changes between events. The models are analyzed by numerical methods. Different runs are needed to test different scenarios due to the randomness of inputs. Therefore, the final result encompasses almost every possible situation.

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil 3. Literature Review

In this section, a study in AGV simulation field has been carried out by investigating former researches and scientific papers, which could help to understand the problem and develop this project. There is a main focus on the optimal number of AGVs needed in a system.

AGVs optimal number

3.1.1 AGVs optimal number in Flexible Manufacturing Systems

Some articles about Flexible Manufacturing Systems talk about the significance of an optimized and efficient AGV system to improve its performance (Fethi and Mehdi (2019), Mahadevan & Narendran (1993) and Rajotia et al. (1998)). When a flexible production is needed due to several aspects, such as different variants of the product, several handling materials paths or different volume production, AGV systems come as the most appealing solution for companies to develop their manufacturing systems.

Rajotia et al. (1998) proposed an analytical model to obtain the optimal AGV fleet size. First, it is necessary to know the time an AGV spends in the system, using three points: an origin point, the load pickup point and the unload delivery point. They applied several mathematical equations and parameters to obtain the number, focusing on calculating the empty vehicle travel time to get a more accurate result. Even though an exact simulation of the problem cannot be performed due to its complexity, this method is useful to simplify the problem and to obtain an initial result, which can be used as a good starting point for the simulation process and it will give a better understanding of the system and the number of AGV needed.

Fethi and Mehdi (2019) studied three different networks with Arena software, each of them simulated from 1 to 7 AGVs. According to productivity, once it was reached the highest value, increasing the number of AGVs decreased productivity or kept it at the same level. It happened the same with the total time spent to complete all the jobs (from the first one to the last one). Once they got the lowest time value, more AGVs did not decrease the time spent in the process. Finally, related with the Waiting Time, as they increased the number of AGVs, the waiting for transfer time was reduced, but the waiting for process time was increased, so they had to look for a balance between them. As a conclusion, exceeding the number of AGVs needed in the manufacturing system it is useless and may produce a reduction of its performance, as it can be seen in Figure 1 and Figure 2.

According to Mahadevan and Narendran (1993), jobs undergoing, processing times, volume mix, number of machines, capacity and traffic intensity are important data that influence in the decision- making of the fleet size. In the experiment, when the number of AGVs was low, there was a high throughput time and a decrease in the production volume. On the other hand, overestimating the number of AGVs needed, only reduced the throughput time, queue time and waiting time.

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil

Figure 1. The effect of AGVs number variation on productivity (Fethi & Mehdi, 2019)

Figure 2. The effect of AGVs number variation on the total time spent per AGV (Fethi & Mehdi, 2019)

Vivaldini et al (2016) propose if the complete vehicle journey for material handling tasks is known, then the minimum number of AGVs needed in the system can be determined. An approximation can be obtained by adding the travel times (loading, unloading and waiting time) divided by the available time of the AGV. Deterministic, stochastic and analytic models can be used to obtain the right amount of AGVs. Also, AGVs features such as capacity, speed, battery life and guidance type can affect the size of the fleet.

The number of AGVs must not be overestimated, as it could produce congestion in the system, as well as for economic reasons. Therefore, some methodologies have been developed to achieve the desired result, like the Task Assignment Module. Step by step and applying a recurrent procedure, the right AGV number for the system is achieved.

3.1.2 AGVs optimal number in Port Container Terminals

One of the fields where AGVs are getting more relevant is in Port Container Terminals (PCT). Containers must be transported from the ship to the stack and vice versa as quickly as possible, to minimise the time the ship spends at the berth and improve the efficiency, so AGVs come as the best solution for the transportation of containers in the terminal. The main task is to obtain the right number of AGVs and their utilisation (Pjevcevica et al., 2017). Also, once the optimal number has been chosen, optimal routes have to be established. All those aspects must be done before the vehicle dispatching.

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil

In the fleet sizing decision-making process, several data are considered like maximizing vehicle utilisation and throughput or minimizing queue length. They used a polynomial-time algorithm to solve the problem from a theoretical point of view (Vis et al., 2001).

It is important to dimension properly the fleet, as a big number of AGVs decreases utilisation time and a low number increases the ship service time. To avoid collisions, algorithms are used to postpone or re-route the newly AGV in the system.

A simulation with ARENA was carried out from 6 to 15 AGVs to obtain different results (Pjevcevica et al., 2017). When the number of AGVs was increased, average ship service time, the average utilisation rate of AGVs and the operating costs of Quay Cranes were decreased, while the average utilisation rate of Quay Cranes and average operating costs of AGVs were increased. The average number of handled import containers per ship did not follow any rule, but the highest number was obtained with 11 AGVs. Even though it was not possible to obtain a unique result due to all the data involved, a small number of possible solutions could be found, so the task to achieve the right solution for the system was simplified.

3.1.3 AGVs optimal number with a queuing system

Choobineh et al. (2012), proposed a linear programming model based on closed queueing networks to obtain the minimum fleet size for an AGV system. The results were compared with the ones provided by simulation software, concluding that even though the simulation results were more accurate, this model helps to reduce the possible solutions to simulate within the software tool.

Conclusion

As a conclusion of the literature review, a general procedure method has been found. Mathematical tools are sometimes used to obtain an approximation of the optimal number of AGVs needed in a system, and afterwards, a simulation software tool is used to simulate the model with different numbers above and below the predicted one, as simulation tools provide more accurate results.

Furthermore, using more AGVs than needed can produce congestion in the system, a decrease in the production rates and an increment in the cost of the system, so the calculation of the optimal number of AGVs comes as a crucial part of the process.

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil 4. Method

A methodology is crucial to fully understand and develop the project. To study and solve the problem, a series of steps must be followed to achieve the results satisfactorily. Banks et al (2010) provide the simulation methodology is needed to develop this project. In Figure 3, all the different steps in the process can be observed. Moreover, three main milestones can be identified.

Figure 3. Methodology flowchart

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil

Problem understanding

Before beginning the project, it is necessary to have a good understanding of the problem. As the company has a specific issue to be solved, information from several sources has to be collected.

Problem Formulation: A real case is going to be simulated, thus the company will have to provide all the specifications and requirements of the project, as well as the data of the real case. Everything must be well explained and understood to avoid further issues.

The setting of objectives and overall project plan: The questions that need to be answered within the project and the different scenarios in the simulation must be determined. Moreover, a plan to achieve the different steps on time is also created.

Literature review: detailed research must be conducted in this step to acquire all the knowledge needed for the project to be accomplished. Former articles and projects are useful to discover what researchers previously did relate to the problem in question, in this case, AGV number optimization.

Software research

Software survey: For the software survey, research in former projects and articles, as well as the official webpages of the software will be read to look for the most suitable software for our problem. A chart to compare the different features of each one will be done, as well as the pros and cons. The focus will be mainly on which one provides the best data to obtain the optimal number of AGVs. Furthermore, visual graphics and other data supply will be taken into consideration during the choice of the software.

Simulation process and results

Model Conceptualization: a model based on the real one should be created. For this purpose, some assumptions will be done, and a very simple model will be developed. Later on, more detailed information will be added until a good model is finally accomplished. More complexity is time- consuming, and it does not provide better results than a simple but accurate one.

Data collection: All the necessary data for this project already exist. It will be provided by the company via software and CAD drawings, where all the AGV speeds, stations and distances can be consulted.

Model translation: once the model is ready, it needs to be coded and programmed in a software tool. In this case, the software tool used in the project will be chosen after the market survey research abovementioned.

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil

Verified: In this step, the proper performance of the model will be verified. It is recommended to verify the model during the process and not to wait until it is finished, as mistakes will be more difficult to be solved.

Validated: It is important to make sure the model developed represents the real one satisfactorily. In this case, as the model already exists, both outputs will be compared to validate the model developed.

Experiment design: A deep study must be carried out to figure out how many runs are needed in order to consider the results as valid ones. Also, the length of the simulations and the warm-up time must be defined to trust the output data.

Production runs and analysis: the output data is analysed to know if the number of runs and simulations were enough to consider it valid.

More runs: A decision on if more runs are needed and if more scenarios must be simulated is made according to the analysis abovementioned.

Documentation report: All the output data obtained during the simulation will appear in this report. Moreover, all the information must be motivated and explained to have a better understanding of the problem and the solution. This will also help the client to understand the model and the results, and it will be easier to change something in the future if the report is well explained.

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil 5. Problem formulation

In order to achieve good results and get every question solved, it is important to carry out a deep understanding of the problem during the first steps of the project developing. Therefore, different meetings with several members from Jernbro team took place at the factory along the first weeks and also during the development stage to clarify every possible doubt. After these meetings, different problems were identified.

• FORMER METHOD OF AGV NUMBER CALCULATION

Currently, the AGV optimal number of a system is calculated with Excel. The estimation is based on loops. A loop is a group of processes connected by the same flow thus the first and final task are the same, completing a whole cycle. There can be as many loops as different flows there are in the system. In each loop, several parameters are taken into consideration: distances between stations, average speed, battery usage and charging time.

Two parameters need to be defined: cycle time, which is work process-based, is the time it takes to complete the production of one unit from start to finish. The second parameter is the time the AGV takes to complete one loop. This parameter is obtained by dividing the distances between the average speed of the vehicle plus the loading and unloading time at each station. Once this is calculated, the division of both parameters gives an estimation of the necessary number of AGVs in the system.

Nevertheless, some questions need to be answered: is the method accurate? How long does it take to obtain the results? Is it a complex method? All these inquiries were asked to the person that developed this strategy intending to figure out how it works. These questions were difficult to answer since it depends a lot on each project. The calculation might not be accurate and reliable due to the unpredictable behaviour of AGV systems. For small projects, a rough calculation may be enough, but in larger projects, the method may not work properly, as there are many variables, e.g., blocked sections, deadlocks, available tasks that are challenging to include in an Excel file. It might obtain good results, but there is not a way to check the effectiveness beforehand. Therefore, this method is sometimes based on personal experience rather than an engineering and regular procedure.

• LACK OF VISUAL TOOLS

There are not any graphic tools or virtual models where the system is developed before the construction of the layout. This causes major issues when it comes to explaining the final design of the project to the customer, as usually, models are large, complex and difficult to understand if there is not any visualization of it. Furthermore, communication between departments (mechanicals, IT, PLC programmers) regarding the model understanding can also be tedious.

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil

• INABILITY TO TRY THE MODEL UNTIL IT IS BUILT

It is not possible to know the real behaviour of the system until it is assembled in the factory. This cause some uncertainty about the estimation of the number of AGVs, as well as if the AGV path it is optimized to achieve the best results. Moreover, different scenarios are not possible to be tested prior to this step, which prevents the company to improve their designs without any cost.

• WRONG CALCULATIONS

Adding an extra AGV once the system is built due to the fact that the tasks are not achieved on time is over costing, as the AGVs are designed and produced specifically for each project. Although this problem can be easily solved by increasing the AGV speed at some zones or by changing the battery type, an accurate calculation will avoid this scenario.

Once the problems have been stated, it is time to offer solutions to this situation. What is needed is a tool that allows the employees to develop the model in a virtual way, where what-if scenarios can be tested and where the model can be represented in a 3D environment to have a better understanding on how it works. For this reason, simulation becomes a possible solution to solve these problems. Therefore, a deep study will be carried out in the next section to conclude which simulation software offers the best features for AGV systems and if this tool will provide the benefits the company needs.

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil 6. Software survey

Why is a survey needed?

In this project, a survey has been conducted in the field of AGV simulation software. The company wants to include simulation in their projects to achieve several goals: better results, know the behaviour of the system in advanced, solve any problems they may have during the designing process, obtain the optimal number of AGVs and improve the communication with the customer.

There is a wide range of software available in the market, many of them with a multitude of functionalities and tools that makes simulation a very powerful tool to create almost any model. Thereby, it is necessary to conduct a survey to obtain as much information as possible of every software, to come to a decision on which software is the most proper one according to the company requirements.

Jernbro’s requirements

The company set the features the software must include in order to be a useful tool. A meeting took place at the beginning of the project, where all the specifications were established.

To achieve this task, MoSCoW method is used. This method was developed by Dai Clegg in 1994 (Kukhnavets, 2016). A series of features are evaluated according to their importance. MoSCoW is the acronym of Must, Should, Could and Won´t. Must refers to critical features that the software has to include to be useful. Should refers to features that would be helpful if they are included. Could means that these features do not need to be in the software but they may help, and finally, Won´t means that features are not useful or irrelevant for the company. In Table 1 the requirements are shown along with their importance for the company.

Table 1: MoSCoW method for Jernbro requirements

SOFTWARE SURVEY REQUIREMENTS Must Should Could Won´t Easy to use (user-friendly) X Online Support X Tutorials to learn about the software X 3D Model Implementation X AGV number calculation X Detailed output data, charts and graphics X Import CAD models X Free version X Low price for the student version X

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil

Software comparison

A survey conducted by ORMS Today Magazine in conjunction with The Institute for Operations Research and Management Sciences, INFORMS Analytics and WinterSim Conferences, discloses the best simulation software in the market and their main features and parameters, as well as some advantages and disadvantages (Anylogic, 2017). This is useful to compare and analyse the different options there can be found in the market and obtain an overview of which ones are more suitable for the company and the project.

In Appendix 2: Simulation Software Comparison, eight simulation software for industrial models are compared by highlighting their main features through relevant categories regarding technical attributes, student version availability and AGV module. After deep research in the previous simulation software, where they were analysed following the MoSCoW method, the best four software that complete the company´s requirements properly are Anylogic, Arena, FlexSim and Plant Simulation. A deeper description of each software can be found in Appendix 2: Simulation Software Comparison.

Selection of the final software

To make the final decision about which software fulfils the company´s requirement, Pugh matrix method has been applied. This technique, designed by Professor Stuart Pugh, evaluates several design concepts through different criteria (Burge, 2009). If a concept design satisfies the criteria, receives a positive score, otherwise receives a negative score or null score. These criteria can also be weighed with a higher or lower rate regarding how important it is for the project.

In this case, a change has been done to adapt it to the necessities of the study. Four software are evaluated through the most relevant parameters with a score from one to five, and the software that achieves the higher score will be considered as the most suitable one for this project. It would not make sense to evaluate this software with negative scores since they all satisfy the parameters that are under evaluation. The results can be seen in Table 2.

Table 2: Pugh chart for software election

SOFTWARE SURVEY REQUIREMENTS Anylogic Arena FlexSim Plant Simulation Easy to use (user-friendly) 4 4 5 4 Online Support 5 4 5 4 Tutorials to learn about the software 4 3 5 3 3D Model Implementation 5 4 5 5 AGV number calculation 4 4 4 4 Detailed output data, charts and graphics 5 5 4 4 Import CAD models 4 4 4 4 Free version 3 4 5 4 TOTAL 34 32 37 32

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil

All of them have the quality to be used for AGV models, but FlexSim has the highest score for several reasons. It is user-friendly and has very detailed step by step tutorials, which makes it easy to learn in a short period of time. It has a built-in powerful module for AGVs, where almost every parameter can be under control. Moreover, FlexSim Scandinavia provided us with the license to use the full version of the software for free, which means it can make use of all the advantages and tools to develop a realistic model. Even though FlexSim does not have a specific tool to calculate the optimal number of AGVs, with the Experimenter tool it is very easy to run the model with different numbers of AGVs each time and check which number provides the best results at once.

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil 7. Case of study

The project was developed by Jernbro for a Swedish company. An internal transportation system to move fixtures between the different machines and processes involved was needed to satisfy the demands of the company. In this section, it will be explained how the system works, as well as the functionality of the different machines and the steps of the three different fixtures flows.

Layout

The company manufactures metal details for the automation industry. Therefore, the sequence will follow several heat treatments and blaster processes. There are 8 processes, that can be categorized as follow:

• Three ovens (oven 58, oven 59 and oven 60). • Three blasters (blaster for small parts, manual blaster and blaster for press hardening). • The packcell. • The autotruck.

Figure 4: AGV Path and machinery display

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil

These are the different operations involved in the process. Information about the functioning of these processes has not been provided since it is not necessary to simulate the system. Only information regarding the throughput per hour is needed. The inputs and outputs of the system are the mix tables. The raw material entries through four conveyors. The finished products leave the system through two exits. In Figure 4, it can be seen where all the different machines are located on the floor. The AGV path is pink-coloured and connects the different machines.

Flow Description

In the system, it can be found three different types of flows, although some steps are shared by all of them as

Figure 5 shows. The flow starts in the mix tables, and it can go to any of the ovens. After this step, the fixtures go to one of the blasters in order to create different products. Each blaster has its own next step. Finally, they are all sent again to the mix tables, where they are removed from the system.

4 entrances 2 exits

Figure 5: Flow description AGV Logic

The AGV system works with a master as the brain of the operations. All the different processes of the factory are connected to this network. Therefore, every time there is a new available task, this order is sent to the AGV as a new task. There are 114 stations located all over the wired path where the AGV checks parameters like speed, next station or blocked areas. These stations are Radio Frequency Identification (RFID) tags, thus the AGV has a built-in RFID reader to get all this information. Some of the stations also work like loading and unloading points of fixtures for the machines in the process. The company uses AGVStationEditor to control all these parameters when programming the system, providing the AGV all the information it needs to go to the next station and achieve the next task.

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil

Charging stations

There are two charging stations in the layout as it can be seen in Figure 6. Every time the AGV arrives at the intersection before the charging station, it checks whether their corresponding charging station is available or not. If it is free, the AGV goes to the charging station and remains there for 60 seconds. After that, it leaves to the next station according to its final destination. However, if there is another AGV already recharging, the second AGV takes the other path and continues to its final destination.

The system also considers an extra scenario regarding AGV recharging. If the battery is under 20% of its load capacity, the AGV automatically goes to a charging station and remains there for 20 minutes. This scenario barely happens, since the charging times are designed to avoid this situation. This is a security measure to ensure that AGVs never run out of battery. Moreover, the AGV battery should always be between 20% and 80% of its load capacity.

Figure 6: Charging stations Data Collection

When it comes to building the model, data has to be collected to represent the system in the software. These are the data needed for this model:

• Distances: the floor and the AGV path is designed in a CAD file, where all the lines, curves and distances between stations and processes can be measured. • Speed: with AGVStationEditor, the information regarding the current speed in each station or stretch can be checked. • Next Station: in the software mentioned above, the next station the AGV has to go can also be checked. Depending on the final destination, in some checkpoints, the AGV can choose between different paths.

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil

• Loading/Unloading time: an estimation has been done of 30 seconds per loading or unloading task. • Break distance: there is a safety distance of 0.5 meters between AGVs to avoid collisions when running them all on the layout. • AGV model TC55: they will move the fixtures all around the warehouse. For this project, Jernbro is using a model called TC55. Figure 7 and Table 3 display a 3D model of the AGV and all its different parts.

Figure 7: AGV TC55 3D Model

Table 3: AGV TC55 components

1 Antenna, communication WLAN 10 Battery connection 2 Emergency stop button 11 Electrical cabinet 3 Operator panel, Siemens Touch 12 Cover for electrical cabinet 4 Flashing light 13 Charge inlet 5 Indicator lamps and push buttons 14 Joystick 6 Front laser scanner 15 Drive unit 7 RFID reader 16 Floor change device 8 Fixture connection points 17 Stop plate sensor 9 Battery lid 18 Batteries

It is important to address some of the features from the AGV regarding the simulation model. They can be seen in Table 4.

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil

Table 4: AGV TC55 main features

Length 1200 mm

Width 800 mm

Height 600 mm Max Speed 60 m/min Front Sensor Stop Distance 0.5 meters Battery Capacity 198 A/5h

Battery Idle Use 4 A

Battery Recharge 60 A Battery Recharge Threshold 20% Battery Resume Threshold 80%

• Assignments per hour: The system needs to process a series of pieces to achieve the desired result. This calculation has been done by assignments per hour, so each one of the system machines needs to finish several pieces per hour. In Table 5, all the assignments per hour of every machine are shown:

Table 5: Assignments per hour for every process

Assignments/hour Assignments/hour Piece ready to pick MACHINE Pick Up place Leave place up/leave every… Mix tables 7.5 7.5 8 min Oven 58 2.5 2.5 24 min Oven 59 2.5 2.5 24 min Oven 60 2.5 2.5 24 min Manual blaster 1.9 1.9 31.57 min Press hardening blaster 3.1 3.1 19.35 min Blaster for small parts 2.5 2.5 24 min Pack cell 3.1 3.1 19.35 min Autotruck 2.5 2.5 24 min

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil 8. Development Assumptions

“Genius is the ability to reduce the complicated to the simple” C. W. Ceran.

Although the idea is to get a loyal model from reality, thus it can be used as a graphic tool as well, some assumptions have been done. Following the design principle KISS (Keep It Simple, Stupid), it is important to avoid unnecessary complexity that will make the model developing process too long (Anderson, 2014). As a result, Table 6 shows all the assumptions that will be considered.

Table 6: Assumptions for model developing

REAL SYSTEM MODEL TRANSLATION The layout is divided into two sections. Each Each AGV can move all around the layout. This AGV is allocated to one section, and it can simplifies the model design. For AGV number 1 only move on its section. calculation, it only matters the fact that there is an available AGV to perform the task, rather than which AGV performs it. AGVs transport different kinds of products. The AGV only transports one kind of product. It only 2 matters whether the AGV is carrying something and which is its destination. If the battery is below 20% of its full capacity, This feature is removed since this situation barely the AGV goes to a charging station and stays happens. Charging the AGV for 60 seconds every there for 20 minutes (see chapter 7.4). time it goes to a charging station should be enough to keep the battery level between 20% and 80% of 3 its full capacity. That is why this situation is unusual and can be avoided in the simulation. It is more a security measure to ensure that AGVs do not run out of battery than an important feature to simulate in the system. Every process (oven, blasters…) has a specific The real-time of processing items is not important time of processing items. since what matters is how often a fixture is ready to be transported. As an example, the oven process is 4 around 8 hours, but every 24 minutes a fixture is ready. Therefore, the processing time will be 24 minutes. There are sections in the layout that are These paths have been removed since they are designed to avoid that AGVs go near the redundant for the simulation. 5 operation sections and, therefore, near the factory machinery when it is not necessary.

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil

Model translation with FlexSim

FlexSim is a powerful software for AGV simulation. It provides with an AGV module with lots of features that simplifies the creation of the model. One of the advantages of FlexSim is the use of 3D objects. While other software use blocks, code or 2D objects to create the logic to develop the model, FlexSim is equipped with a library of 3D objects that can represent every situation inside the scope of a model simulation. As a result, models are easier to understand, even for people that are not used to the field of simulation.

8.2.1 Creating the AGV system

Ø Designing the model background

For the model background, the CAD file with the layout and the machinery has been imported. This will provide all the information needed about the location of the different machines and processes inside the factory, including the position of the AGV path along with the warehouse. Whenever the measurement units from the software and the CAD file are the same, the path can be drawn straight on the floor without the need for previous measurement of distances and used of coordinates. This action will save a lot of time while designing the model and will give accurate results.

Ø Building the path

The path has been created using the following tools from the AGV library: straight and curved paths. Some paths from the original layout were taken out since they are only used in the real system for good positioning of the AGV while loading and unloading fixtures. The complexity of this part lies in reproducing with fidelity the real path.

Once the path is finished, the next step is adding all the stations. AGVs get information about the speed and where to go on each of these spots. FlexSim has a tool called Control Points, that it is used to allocate each AGV a new available task, e.g., loading a fixture at oven 58. Those control points act like the stations in the real system.

The AGV speed changes from one station to another depending on how close the curve is, the number of intersections or the available space between the AGV and the factory machinery. Different speeds can be set for each path segment in the model. Therefore, the AGV will run at a speed of between 10 and 60 m/min depending on which section of the layout it is placed.

The AGV has sensors to detect the presence of other AGVs, workers or any object that is on their way. For that reason, it has been set a parameter that will keep a safety distance of 0.5 meters between AGVs. Thus it will automatically stop and resume after the AGV in front of it has started moving again, avoiding any kind of collisions. When an AGV arrives at an intersection, it will keep a safe distance before entering to elude possible crashes.

Finally, FlexSim offers the possibility of adding control areas. These items are zones where only a limited number of AGVs can stay in. This is useful to restrain for an AGV to occupied one path

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil

where only one vehicle can run at a time. Figure 8 shows all the elements explained in the previous paragraphs.

Path intersection

Control Point

Control Area

Figure 8: Different path elements

To simulate the movement of the AGVs along the path, another 3D object will be used. The Task Executer will be in charge of transporting material all around the different stations. Figure 9 shows how it looks like inside the model.

Figure 9: Representation of TC55 using the Task Executer 3D object from FlexSim

Ø Representing the processes

All the processes and stations involved in the production process are out of the scope of this simulation since they are not part of the AGV network. However, it is crucial to take these processes into account since they establish the work pace for the AGVs. The vehicles need to know where to pick fixtures up and where to leave them, and the time it takes to these operations to carry out their work, determining the working load for the AGVs

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil

Every process involved will be simulated as a delay, where the fixture stays there for a certain amount of time. This time coincides with the figures from the fourth column of Table 5. Therefore, for every operation, there will be one buffer at the entrance, limited to one piece; one processor, limited to one fixture per time and its corresponding time as a delay; and finally, one buffer at the exit. This ensures the correct achievement of the unloading and loading times for the AGV, as there will be always one piece to be processed. Figure 10 shows a sample of a station using 3D objects from Flexsim.

Processor

Exit Buffer Entry Buffer

Figure 10: Standard operation station

The mix tables are the entrance and exit of the system, where the trucks deliver raw material and pick up finished fixtures. This is modelled as a source with a buffer to store the products. As a result, the AGV can pick them up when they are allocated with the corresponding task. A sink simulates the exit of the system, as it can be seen in Figure 11.

BUFFER

SINK

SOURCE

Figure 11: Entries and exits of the system

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil

8.2.2 Creating the workflow logic

Once the path and the system are finished, it is time to set the logic of the workflow and the AGV sequence. Figure 12 shows how the whole process looks like.

Figure 12: AGV model in FlexSim

First, the AGVs need to move around the path. A loop will be created, where AGVs will move along the control points looking for tasks to do. This loop is called NextLookForWork and it is represented by the red line that can be seen in Figure 12. All the processors will be connected to the main path through location connections. That means, every time there is a new task available, it will be introduced in a list called AGVWork. Therefore, the AGV knows there is something ready to do and they will go to the corresponding destination always taking the shortest path.

The built-in internal logic works by introducing both AGVs and flow items into lists. To set the flow logic, this means, how the AGV knows which is its destination after loading a fixture at another station, a few lists have been created according to the flow specifications mentioned in 7.2. Depending on whether it is an arrival or departure place, the list will push or pull the flow items.

The AGVs need to recharge their batteries. In the system, there are two charging stations. The AGV stops at the charging station for 60 seconds as long as the station is on its way to its destination and there is not any other AGV already recharging. For a further understanding of the AGV charging logic see Appendix 3: AGV charging logic.

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil

8.2.3 Issues while developing the model

Ø Creating the AGV charging logic

This part of the process has been challenging since the program itself does not provide any tool that makes the AGV stops to recharge for a certain amount of time instead of going to the station every time the battery level is under the specific threshold (20% of load capacity for this situation).

The real system does not behave exactly in the way it has been described. The AGVs are able to stop at a charging station even if they are loaded. FlexSim already built-in logic assigns a destination to an AGV when it is loaded, and it will take the shortest path to achieve that destination. As the charging stations are aside of the main path, they will never take that way. The first idea was to implement the system in that way. However, after so many hours spent in guessing how to accomplish this goal, the conclusion was that too much time had to be spent in solving this tedious task for the minimal improvements there would be obtained. The model will gain in the visual part, where the client could see how the system behaves in reality, but the data obtained for calculating the optimal number of AGVs or the recharging time spent by the AGVs at the charging station will be almost the same, as they will still charge their batteries at other moments.

Ø Deadlocks

A deadlock is an issue that sometimes happens when developing a model with AGVs. In order to understand what a deadlock is, Figure 13 shows an example. Imagine there are two AGVs, one of them placed on Control Point 1 and the other one over Control Point 2. AGV1 wants to go to Control Point 2, but it is occupied by AGV2, and AGV2 wants to go to Control Point 1, but it is occupied by AGV1. As a result, the system blocks because of a circular deadlock, where AGVs cannot move forward. This situation can be solved by adding more control points in the system, creating control areas to limit the number of AGVs in a zone or moving and replacing the already existing control points.

Figure 13: Deadlock example

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil 9. Results Validation of the model

Before analysing the results, it is crucial to make a validation of the model. The real system works with four AGVs. This means, in order to validate the model, the data from the current system has to be compared with the data provided by the model developed with FlexSim using the same number of AGVs. This validation will be carried out by checking the following data: throughput at the exit of the system, assignments per hour achieved in each process and the AGV utilization data with four AGVs.

Focused on the throughput at the exit of the system, it has been obtained a result of 1259 fixtures out of 1260. This means an error of 0.08%. Related with the assignments per hour, Table 7 shows the results.

Table 7: Assignments per hour after running the model for a week

PROCESS REAL DATA MODEL DATA

Oven 58 2.50 2.50

Oven 59 2.50 2.49

Oven 60 2.50 2.50

Blaster SP 2.50 2.50 Blaster M 1.90 1.90 Blaster PH 3.10 3.09 Packcell 3.10 3.09 Autotruck 2.50 2.50

Finally, to complete the model validation, the data regarding the AGV utilization is needed. Comparing the real utilization with the data obtained with the model will fully validate the system if the results are under a certain threshold of error. The problem is this data could only be obtained with the model. There are no statistics regarding this data. As this is a project for a third company, which is not involved in the development of this thesis, it was not possible to get this data. Therefore, it not going to be possible to fully validate the model from the utilization data perspective. However, the results obtained are consistent and prove the system works with four AGVs. Having a look at the utilization data from the model using four AGVs, they are working comfortably, as it can be seen in Figure 14, where travel empty data for each AGV is more than 50% of the time they spend in the system.

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil

Figure 14: AGV Utilization data for 4 AGVs The optimal number of AGVs

Once the validation has been completed, it is time to analyse the results to obtain the optimal number of AGVs. The model works with four AGVs but, is this the scenario where the system gets the highest throughput using the smallest number of AGVs without overloading them? The optimal number of AGVs is the one that lets the system achieve the desired throughput on time. Besides, it must be affordable from an economic point of view, as it might be that an extra AGV can achieve more throughput, but not enough to justify the cost of that extra AGV. To check this, several scenarios are going to be tested, where each scenario has a fixed number of AGVs. In particular, 10 cases are tested, from one to ten AGVs.

Since all the input data is constant, only one run is needed per scenario. The system has 2.5 hours of warm-up time to make sure that the system is working at capacity. The simulation horizon time will be one week, as the system is designed to run 24 hours a day and 7 days a week.

Ø Throughput at the exit of the system

1400

1200

1000

800

600

Exit throughput 400

200

0 Scenario Scenario Scenario Scenario Scenario Scenario Scenario Scenario Scenario Scenario 1 2 3 4 5 6 7 8 9 10 throughput 345 1174 1258 1259 1260 1260 1260 1260 1260 1260 Number of AGVs

Figure 15: Exit throughput according to the number of AGVs

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil

The system was designed to have an input/output of fixtures of 7.5 assignments per hour. After a week running the model, the throughput in the exit of the system must be of 1260 fixtures. Figure 15 shows the results of the experiment. The model has been tested in 10 different scenarios, where the only parameter that has been changed is the number of AGVs, from one to ten.

In light of the results, it is useless to use more than five AGVs, as the throughput remains the same quantity and with the desired value of 1260 fixtures. It is interesting to carry out a study with the results from two to five AGVs, as one AGV does not produce good results (it can only achieve 27.4% of the total production).

Ø Assignments per hour

One way to know if the AGVs complete the tasks on time is to check if they achieve the assignments per hour in each process of the system. Therefore, this parameter is analysed with an AGV number from two to five.

3,5 3 2,5 2 1,5 1

Assignments per hour 0,5 0 oven58 oven59 oven60 BlasterSP BlasterM BlasterPH Packcell Autotruck REAL DATA 2,5 2,5 2,5 2,5 1,9 3,1 3,1 2,5 2 AGVs 2,5 2,49 2,49 2,48 1,9 3,04 2,88 2,48 3 AGVs 2,5 2,49 2,5 2,5 1,9 3,09 3,08 2,5 4 AGVs 2,5 2,49 2,5 2,5 1,9 3,09 3,09 2,5 5 AGVs 2,5 2,49 2,5 2,5 1,9 3,1 3,09 2,49 Machines and processes

REAL DATA 2 AGVs 3 AGVs 4 AGVs 5 AGVs

Figure 16: Assignments per hour and number of AGVs

As shown in the results of Figure 16, it can be said that three or more AGVs satisfy the assignments per hour of the system.

However, these results regarding the assignments per hour can be tricky. It is important to see as well the Work-In-Progress (WIP) remaining at the buffers once they have been processed. Figure 17 shows the final state of the model after one week of simulation. Although the manual blaster has processed all the fixtures, the AGV had no time to carry all of them to the mix tables. As a consequence, the exit buffer of the manual blaster has a WIP of 48 fixtures. That

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil

is the reason why the desired throughput is not reached with two AGVs. From three AGVs and on, there is no WIP left at the operations, since the AGVs are able to fetch all the fixtures on time.

Figure 17: Total WIP after running the model with 2 AGVs

Ø AGV utilization data

Some parameters related to AGVs have also been evaluated to obtain useful information about their work, as shown in Figure 18. In one hand, we have the parameters that increase with a higher number of AGVs:

• Recharging time: the AGVs spend more time without performing a task; thus they can go to the charging station more often. • Blocked time: the more AGVs in the system, the more they have to wait to allow other AGVs to move in case of intersections, restrained areas or loading and unloading tasks. • Travel empty time: there are more AGVs to perform each task.

On the other hand, the parameters that decrease when the number of AGVs is bigger are travel loaded time, as the tasks are split between a higher number of AGVs, as well as loading and unloading time, for the same reason explained before.

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil

30 Total time [%] 20

0 Recharging Blocked Travel empty Travel loaded Loading Unloading AGV utilization data

2 AGVs 3 AGVs 4 AGVs 5 AGVs

Figure 18: AGV Utilization data depending on the number of AGVs

Ø Travelling time

Moreover, in Figure 19, the time a fixture spends since its ready until an AGV picks it up and leave it in the next station also decreases when the number of AGVs increases, as it is expected.

4500

4000 3500

3000

2500 2000 1500 Time [seconds] 1000

500 0 Autotrack to BlasterSP to BlasterM to MixTables to Ovens to BlasterPH to Packcell to MixTables Autotrack MixTables Ovens Blasters Packcell MixTables AGV possible routes

2 AGVs 3 AGVs 4 AGVs 5 AGVs

Figure 19: Time since a fixture is ready until it arrives at the next station

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil 10. Discussions

Once the more relevant results have been presented, it is possible to discuss which option suits more the specifications and requirements of the system.

It was interesting to find out that in this model, the exit throughput remained constant despite increasing the number of AGVs. Usually, in some AGV systems, the throughput decreases when the number of AGVs increases, as there are many blocked situations and lack of room for all AGVs. This results in congestion that makes it impossible for the system to achieve the desired throughput. In this case, the layout is so big and the assignments per hour so low that increasing the number of AGVs does not produce any congestion in the system, as there is enough room for all of them. Therefore, the data analysis was focused on five or fewer AGVs, as the system does not need them and it would cost extra money to the company.

The option with two AGVs can be rejected, as it does not achieve the assignments per hour and the exit throughput the system needs. Moreover, in the simulation run, it could be observed how the fixtures were stuck in some places, and the buffers were increasing their sizes due to an incapacity of the system to perform all the tasks on time.

When there are three, four and five AGVs they all achieve their commitment to performing the tasks on time. It can be appreciated that there is just a difference of one fixture between the results. Even though five is the first quantity of AGVs that achieves all the exit throughput in a week (1260), three and four are so close that it can be considered that all of them can achieve the task; because when the simulation stopped, there was some WIP about to be ready. Moreover, in all three simulation experiments, there was no congestion in the traffic flow, and no fixtures were stuck or accumulated at any place during the running of the model. So, which option is the best one?

The lowest number able to achieve the proper performance of the system is three. However, it might be considered that despite the failure parameter of the AGVs is 0.02%, problems can occur, such as reparation of broken AGVs or possible daily maintenance. As a consequence, the system will not be able to perform the tasks on time. Moreover, as this is a simulation, results may differ from the real ones.

A fifth AGV it is not necessary, as it only provides one more fixture and the cost of an extra AGV does not support this decision. The company needs to remain competitive in the market, offering projects at the lowest budget as possible. Therefore, the optimal number of AGVs for the system is three. Nevertheless, it is a risky decision to use only three, since the production will have to stop in case one AGV breaks. So, staying on the side of security, the proper solution for the system will be four AGVs.

Regarding the validation of the model, it is not to possible to say it is completely validated, as it was not feasible to obtain the AGV utilization data from the real AGV system. The model behaviour is satisfactory, as it achieves the exit throughput and the assignments per hour in every station. What it was not possible to validate is the workload of the AGVs, as this information is missing. However, the workload in the simulated model is low, thus the system can work smoothly. For these reasons, it is

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil concluded that even though the system cannot be 100% validated, the results can be reliable, as the model works according to specified flow and assignments per hour.

The simulation methodology used in this project has been useful in order to achieve the aim and objectives, as well as to structure the report and all the steps that have been accomplished during this process. One of the problems was to not carry out deeper research in some steps of the methodology, as the data collection. We focused on the data needed to build the model instead of both model- building and validation data. This shows the importance of having a well-structured methodology and plan before developing a project to avoid future issues that may not be possible to solve on time.

Finally, from a sustainability point of view, simulation provides an efficient, cost-saving and environmentally friendly way to develop projects, as they remain in a virtual environment until it is built in the real world. This helps to avoid wasting unnecessary resources, as well as improving the green footprint.

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil 11. Conclusions

After carrying out all the research, there is one last question to answer: is simulation worth it against the former method of calculation?

To solve this question, let´s analyse every feature regarding simulation tools. Focusing on the negative aspects, learning a software tool is not always an easy task. In fact, this part has been the one that has taken most time, as there are hundreds of functionalities to explore and learn. But once the knowledge is acquired, building a model is a feasible task in a short-term period. A realistic 3D model of the AGV system provides the customer with accurate results and nice graphics that may help understand the functioning of the model, as sometimes the clients do not have the knowledge or experience to understand how the system behaves. Moreover, it is possible to get real-time information about transporting times between stations, analyse the workflow and obtain all kind of data regarding the usage of AGVs, which will help to acquire a better understanding of the system.

Optimization of the layout is out of the scope of this thesis, but this tool will help the company achieving better layouts and results when designing an AGV system. Different situations can be tested in order to try several what-if scenarios. This will allow working directly with partners to create better arrangements of the machinery at their facility. While analysing the workflow for this project, it has been concluded that the location of the machines was inefficient. As a consequence, the layout is chaotic and the AGVs sometimes perform longer and unnecessary movements to carry the workload. However, changing the layout of an existing process is not always feasible due to location and cost issues. Nevertheless, is not that unusual that companies assume their layouts are well placed, and different configurations are not tried even if they are possible.

Getting back to the question formulated at the beginning of this chapter, if the purpose is to use this tool for only calculating the optimal number of AGVs, then the answer is that it is not worth it. Simulation software is an expensive tool and sometimes time-consuming. The former method of calculation offers quite good results in the vast majority of cases in a faster and cheaper way. The problem of this method relies on the inability of checking whether the results are reliable or not. It works most of the time because the person in charge of the method has acquired the knowledge to understand how many AGVs a system needs.

However, what if this tool is not only used for the purpose mentioned above, but also for all the benefits mentioned in the first paragraphs of this chapter. Then, the answer is that it is worth it. This tool will offer a new range of possibilities for the company. It will improve communication and synergy between different departments, as it is possible to see the functioning of the system beforehand for a better understanding in every field and thus, make improvements in the system.

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil 12. Future Work

If Jernbro decides to include simulation as a tool, the first decision will be choosing the software. In this thesis, FlexSim was used for all the easiness it offers, but any of the other four finalist software would also be a great tool. Each program has its pros and cons, but they are all considered powerful tools in AGV simulation. The decision may be conditioned by the cost of each software.

Once they have chosen the software, they can try to validate more projects that they are already operating. This is an easy and good way to train the staff in learning the software and getting used to it. They can check which projects are working with an optimal number of AGVs and which ones are running with more or less than needed.

Using simulation to invest time in investigation and development. They can go through different procedures of charging AGVs to see which one provides the best results, as well as testing more efficient ways of moving the AGV. New technologies have been developed where the AGV can move without a wired path.

They can also explore the field of emulation. As they use Siemens for the PLC programming, using Plant Simulation might be useful for creating an accurate model of the system, and later connect the PLC logic to run the system in real life. In this sense, they are using simulation and emulation at once.

Finally, regarding the project developed in this thesis, there are some improvements to be still achieved:

• The logic for the battery recharging system can be enhanced to make the system behaves as it does in reality. • A better data collection to fully validate the model. • Test this project in other simulation tools to see how the results differ or approach from one software to another.

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil List of references

Anderson, A. R., 2014. Forbes. [Online] Available at: https://www.forbes.com/sites/amyanderson/2014/02/27/keeping-it-simple-doesnt- mean-youre-stupid/#5ab32dc572ca [Accessed 3 May 2020].

Anylogic, 2017. Simulation Software Comparison. OR/MS Today Magazine, pp. 1-12. AnyLogic, 2020. AnyLogic. [Online] Available at: https://www.anylogic.com/features/ [Accessed 5 March 2020].

Arena Simulation Software, 2020. Rockwell Automation. [Online] Available at: https://www.arenasimulation.com/industry-solutions/industry/logistics-simulation- software [Accessed 5 March 2020].

Banks, J., Carson, J. S., L.Nelson, B. & Nicol, D. M., 2010. Discret-Event System Simulation. 5th ed. New Jersey: Pearson.

Burge, S., 2009. The Systems Engineering Tool Box. [Online] Available at: https://www.burgehugheswalsh.co.uk/uploaded/1/documents/pugh-matrix-v1.1.pdf [Accessed 2 April 2020].

Chihani, A., 2018. What is an AGV. [Online] Available at: https://www.flexqube.com/news/what-agv/ [Accessed 24 February 2020].

Choobineh, F. F., Asef-Vaziri, A. & Huang, X., 2012. Fleet sizing of automated guided vehicles: a linear programming approach based on closed queing networks. s.l., International Journal of Production Research, pp. 3222-3235.

EAE Business School, 2018. Tipos de sistemas de producción industrial y sus características. [Online] Available at: https://retos-operaciones-logistica.eae.es/tipos-de-sistemas-de-produccion-industrial-y- sus-caracteristicas/ [Accessed 2 March 2020].

Fethi, A. & Mehdi, S., 2019. The effect of AGVs number on a flexible manufacturing system.

Elazig, Turkey, International Conference on Applied Automation and Industrial Diagnostics (ICAAID), pp. 1-5.

FlexSim, 2020. FlexSim. [Online] Available at: https://www.flexsim.com/flexsim/ [Accessed 5 March 2020].

Gilchrist, A., 2016. Industry 4.0: The Industrial Internet of Things. Bangken: Apress.

Groover, M. P., 2015. Automation, Production Systems, and Computer-Integrated Manufacturing. 4th ed. Upper Saddle River: Pearson.

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil

HHI, 2019. Manual vs. automated material handling: know the difference. [Online] Available at: https://www.hhilifting.com/2019/07/29/manual-vs-automated-material-handling/ [Accessed 6 March 2020].

Holstain, W. K. & Tanenbaum, M., 2012. Production system. [Online] Available at: https://www.britannica.com/technology/production-system [Accessed 4 March 2020].

Jernbro , 2019. Sustainability is a far-reaching responsability. [Online] Available at: https://jernbro.com/en/about-us/sustainability/ [Accessed 2 March 2020].

Kukhnavets, P., 2016. GanttPro. [Online] Available at: https://blog.ganttpro.com/en/prioritization-techniques-and-methods-for-projects-with- advantages-of-moscow-model/ [Accessed 2 April 2020].

Law, A. M., 2007. Simulation Modeling Analysis. 4th ed. s.l.:McGraw Hill.

Mahadevan, B. & Narendran, T. T., 1993. Estimation of number of AGVs for an FMS: an analytical model. s.l., International Journal of Production Research, p. 1665.

Massey, T., 2017. A Guide To The Basics of Successful Material Handling. [Online] Available at: https://www.flexqube.com/news/guide-basics-successful-material-handling/ [Accessed 5 March 2020].

Mebratu, D., 1998. Sustainability and sustainable development: Historical and conceptual review. Environmental Impact Assessment Review, 18(6), pp. 493-520.

MHI, 2012. What is an AGV?. [Online] Available at: http://www.mhi.org/downloads/industrygroups/agvs/elessons/what-is-an-agv.pdf [Accessed 24 February 2020].

Pjevcevica, D., Nikolica, M., Vidicb, N. & Vukadinovica, K., 2017. Data envelopment analysis of AGV fleet sizing at a port container terminal. s.l., International Journal of Production Research, pp. 4021- 4034.

Rajotia, S., Shanker, K. & Batra, J. L., 1998. Determination of optimal AGV fleet size for an FMS. s.l., International Journal of Production Research, pp. 1177-1198.

Ray, S., 2008. Introduction to materials handling. 1st ed. New Dheli: New Age International Publishers.

Sasaki, A., Masuyama, S. & Yamakawa, E., 1992. Theoretical Analysis on the Allowable Number of Vehicles in Automated Guided Vehicle Systems. s.l., Electronics and Communications in Japan, pp. 46- 53.

Schwab, K., 2016. The Fourth Industrial Revolution. Cologny: World Economic Forum. Seila, A. F., 1995. Introduction to simulation. Athens, Georgia, IEEE.

Shannon, R. E., 1998. Introduction to the art and sceince of simulation. Washington DC, IEEE. Sinriech, D. & Tanchoco, J. M. A., 1992. An economic model for determining AGV fleet size. s.l., International Journal of Production Research, p. 1255.

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil

Tecnomatix Plant Simulation, 2020. Siemens. [Online] Available at: https://www.plm.automation.siemens.com/media/store/en_us/Tecnomatix%20Plant%20Simulation _7541_tcm29-2062.pdf [Accessed 5 March 2020].

Vis, I. F. A., Koster, R. d., Roodbergen, K. J. & Peeters, L. W. P., 2001. Determination of the number of automated guided vehicles required at a semi-automated container terminal. s.l., Journal of the Operational Research Society, pp. 409-417.

Vivaldini, K. et al., 2016. Integrated tasks assignment and routing for the estimation of the optimal number of AGVS. s.l., International Journal of Advanced Manufacturing Technology, pp. 719-736.

WCED, 1987. Our Common Future, s.l.: Oxford University Press.

Zhang, P., 2010. Industrial control system simulation routines. s.l.:Elsevier.

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil Appendices

Appendix 1: Work Breakdown and Time Plan

Figure 20: Updated Timeplan

Figure 21: Original Timeplan

The original time plan was not specified with so much detail as the updated one. However, the project has been developed following the time plan on time. Some sections such as the frame of reference and software learning have taken more time than expected.

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil Appendix 2: Simulation Software Comparison

Table 8: Software Comparison I

SUPPORTED OPERATING Windows, Mac Windows Windows Windows SYSTEMS COMPATIBLE SOFTWARE Excel, OptQuest OptQuest - Excel, C++ INPUT DISTRIBUTION 31 predefined Arena Input Autofit (internal) ExpertFit FITTING distributions Analyzer GRAPHICAL MODEL YES YES YES YES CONSTRUCTION Reports, Charts, Arena Output Full suite of OUTPUT ANALYSIS Experiment Model execution Analyser and charts and graphs SUPPORT Wizard (internal) logs Process Analyser in Dashboard Opt Quest, Third party Optimization OPTIMIZATION OptQuest algorithms optimizers engine-OptQuest RUN TIME DEBUG YES YES YES YES PROGRAMMING/ACCESS

TO PROGRAMMING YES YES YES YES MODULES Experiment BATCH RUN/ Flexible user Experimentation Process Analyser Wizard / Scenario EXPERIMENTAL DESIGN interface engine Manager COST ALLOCATION YES YES YES YES ANIMATION YES YES YES YES ANIMATION EXPORT YES YES YES YES REAL-TIME VIEWING YES YES YES YES 3D ANIMATION YES YES YES YES CAD DRAWING IMPORT YES YES YES YES Company´s CONSULTING AVAILABLE YES YES YES partners TRAINING COURSES AND YES YES YES YES DISCUSSION AREA STUDENT VERSION YES YES YES YES

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil

Table 9: Software Comparison II

SUPPORTED OPERATING Windows Windows Windows Windows SYSTEMS OptQuest, Excel, Excel, OptQuest, Excel, OptQuest, COMPATIBLE SOFTWARE Excel, Stat::Fit Microsoft Azure Stat::Fit Stat::Fit INPUT DISTRIBUTION 16 statistical ExpertFit and Custom options 22 predefined FITTING distributions Stat::Fit and Stat::Fit distributions GRAPHICAL MODEL YES YES YES YES CONSTRUCTION Datafit, charts, OUTPUT ANALYSIS Output Viewer, graphs, SMORE Plots - SUPPORT Excel bottleneck analyser Layout Optimizer, OPTIMIZATION SimRunner OptQuest OptQuest Dynamic Programming RUN TIME DEBUG YES YES YES YES PROGRAMMING/ACCESS

TO PROGRAMMING YES YES YES YES MODULES BATCH RUN/ Scenario Experiment Scenario Manager Manual scenarios EXPERIMENTAL DESIGN management Manager COST ALLOCATION YES YES YES YES ANIMATION YES YES YES YES

ANIMATION EXPORT - YES - YES REAL-TIME VIEWING YES YES YES YES 3D ANIMATION YES YES YES YES CAD DRAWING IMPORT YES YES YES YES CONSULTING AVAILABLE YES YES YES YES TRAINING COURSES AND YES YES YES YES DISCUSSION AREA STUDENT VERSION 30 $ 25 $ 1995 $ YES

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil

Deep description of the four-finalist software

Arena Arena is a Discrete Event Modelling software, ideal for understanding how already existing models or new ones work without spending any money (Arena Simulation Software, 2020).

Some of the main features of Arena are the flowchart modelling methodology, where it can be built your system by using blocks from a pre-defined library without using custom programming. It is also possible to defined paths and routes for simulation in a 2D or 3D environment where it can also be created animations to visualize how the system works. The data can be introduced in the system by using a wide range of statistical distribution options for the variability of the process, as well as data reports for the output results with largely statistical analysis, graphs and charts.

Some of the advantages Arena has are the possibility of improving the former system by applying new methods or procedures without interrupting the ongoing system. Problems can be fixed thanks to the diagnoses, as well as reducing operating cost, delivery and queuing times and eliminating bottlenecks. On the economic side, profitability and financial forecasting are improved as a result of better control and understanding the overall system.

Anylogic Anylogic is one of the leading companies in the simulation field. It is equipped with a single platform that allows the customer to simulate all type of systems no matter the complexity. It has a multimethod modelling environment where you can run Discrete Event, agent-based and dynamic systems, being the only one in the market that can do this (AnyLogic, 2020).

It has specific libraries for several fields. For this project, Process Modelling and Material Handling are the main libraries. With these, digital factory models can be created, where optimization, transportation and inventory and material flow delays can be improved. A lot of functionalities can be used, like evaluating layout alternatives to improve the system performance, test the production line design capacity, include factory resources that help to eliminate unexpected bottlenecks or anticipate system behaviour in case of breakdowns. Moreover, the different processes and machines in the system can be easily connected between them by using conveyors or AGVs. The AGV fleet interacts with other equipment in the system, and they can also move without using a guided path. It avoids collisions and resolves deadlocks.

Related with visualization, Anylogic has very powerful tools. Flowcharts can be transformed into 3D and 2D graphics with high quality. All the objects are represented, and it is also possible to include your CAD designs to the project, as well as intuitive navigation and controls. It also has a wide range of statistical distributions to include data from different data storage systems, and several charts, graphs and tools to represent the output data from the system, making it simple to understand the solution from the system, as well as optimization tools that help the user to apply the necessary changes into the model. A lot of tutorials, examples and technical support are available to solve any question the user may have during the process of building the model.

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil

Tecnomatix Plant Simulation This discrete-event simulation software developed by Siemens, enable the user to build realistic models to simulate, visualize, analyse and optimize already existing ones or to develop in advance models for new factories without needing to invest money and put the company in risk (Tecnomatix Plant Simulation, 2020).

Some of the main features this software has are great graphical outputs to analyse throughput, bottleneck detection, resource utilization like personnel, buffers, machines and so on; optimizing energy usage and minimize WIP; open system architecture to develop models in multiple interfaces, an intuitive user interface to learn quickly with multiple libraries, where you can extend them by adding your objects; and a highly efficient 2D model view that it can be easily converted into 3D with spectacular graphics to see how the real flow would work in the factory.

Besides, it has an AGV module where path systems can be created and the AGVs can interact with the different processes to handle the material transportation inside the factory. Moreover, Plant Simulation has recently developed AGV Pool, where AGVs can move from one station to another without needing a physical path. The AGV detects where is the station and it will follow the shortest way taking in consideration all the physical elements there might be on the floor. Good output data is provided, as well as the possibility of running your experiments where you can program features like the optimal number of AGVs needed to achieve the desired throughput. As a disadvantage, there aren´t many tutorials about AGVs models and it makes difficult to learn the software on your own without any technical support.

FlexSim FlexSim is a software specialized in industrial and logistics systems for discrete-events simulations. It has powerful 3D graphs that allow the user to work in a more realistic environment and emulating the real appearance of the system. It includes a wide range of statistical distributions for input data to show the variability of the real process, as well as several What-if scenarios to see how the model evolves (FlexSim, 2020).

According to the layout, FlexSim is very user-friendly, as you can pick and drop objects and processes at any place you desire from the toolbar. You can include your designs to the project, as you can import CAD files or plans from the warehouse. Moreover, you can create paths for transportation by selecting the straight line or curve option.

When it comes to building the model, FlexSim has a Standard Object Library with a wide range of objects you can immediately use in the project, accompanied by a pre-programmed list of properties for the objects. In the last versions, Process Flow is included as a powerful tool that uses flow schemes to represent complex logics more efficiently. Furthermore, FlexSim has its programming language similar to C, FlexScript, where you can increase the personalization of the model.

About the output data, FlexSim offers a wide range of detailed graphs and charts. ExpertFit is also included and it lets you know which statistical distribution is more suitable for the output data you obtained.

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil

FlexSim is the preferred option when it comes to AGV simulation, as it includes an AGV module that makes very easy and intuitive to build a model. It also has a very helpful and detailed step by step tutorials. Finally, all the information required to achieve the model can be found in the help tool.

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil Appendix 3: AGV charging logic

Figure 22: Recharging logic

Here, the AGV logic for the charging station is explained. The system is continuously looking for available tasks. Meanwhile, the system is asking itself if there is an AGV over the control points CP1615 and S1528. Those control points are placed in the intersection before entering the charging stations. If there is an AGV already recharging, the AGV will take the B option. That means, the AGV will not access the charging station and will take the other path and continue its task. But, if there is not any AGV recharging at that time, the AGV will go to the charging station (Travel) and will start recharging during 60 seconds (established by the delay function). After that, it will take the C option and return the parking spot thus it can be free for the next AGV.

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil

Appendix 4: AGV Utilization Data

AGV utilization data with 2 AGVs AGV utilization data with 3 AGVs

45 60 40 50 35 40 30 25 30

20 20 Total[%] time

Total[%] time 15 10 10 5 0 Travel Travel Recharging Blocked Loading Unloading 0 empty loaded Travel Travel Recharging Blocked Loading Unloading empty loaded AGV1 12,42 2,28 49,49 20,41 7,7 7,7 AGV1 6,79 1,22 39,46 29,78 11,37 11,37 AGV2 11,41 2,33 48,67 20,75 7,92 7,92 AGV2 6,64 1,3 39,51 29,81 11,37 11,37 AGV3 11,93 2,48 49,53 20,53 7,77 7,77 AGV utilization data AGV utilization data

AGV1 AGV2 AGV1 AGV2 AGV3

AGV utilization data with 4 AGVs AGV utilization data with 5 AGVs

60 70 60 50 50 40 40 30 30

Total tim [%] Total tim [%] 20 20 Total[%] time 10 10 0 Travel Travel Recharging Blocked Loading Unloading 0 empty loaded Travel Travel Recharging Blocked Loading Unloading empty loaded AGV1 18,27 3,09 57,04 12,41 4,6 4,6 AGV1 15,92 2,57 54,06 15,72 5,86 5,87 AGV2 18,07 3,19 57,21 12,18 4,68 4,68 AGV2 16,31 2,72 54,72 14,9 5,67 5,67 AGV3 15,79 3,05 58,7 12,76 4,85 4,85 AGV3 16,21 2,55 53,7 15,64 5,95 5,95 AGV4 16,35 3,22 58,71 12,32 4,7 4,7 AGV4 14,39 2,69 55,72 15,39 5,9 5,91 AGV5 16,9 3,15 58,7 12,11 4,57 4,57 AGV utilization data AGV utilization data

AGV1 AGV2 AGV3 AGV4 AGV1 AGV2 AGV3 AGV4 AGV5

Figure 23: AGV utilization data in scenarios from 2 to 5 AGVs

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil

Appendix 5: Simulation model video example

Here there is a video where the behaviour of the system and all its functionalities can be seen.

AGV Simulation Video using FlexSim

SIMULATION-BASED AUTOMATED GUIDED VEHICLE SYSTEM CAPACITY CALCULATION Fernando Martínez Gil University of Skövde – School of Engineering Science Mario Martínez Gil