This is a repository copy of Introduction to Modeling and Simulation Techniques. White Rose Research Online URL for this paper: http://eprints.whiterose.ac.uk/135646/ Version: Accepted Version Proceedings Paper: Yin, C and McKay, A orcid.org/0000-0002-8187-4759 (2018) Introduction to Modeling and Simulation Techniques. In: Proceedings of ISCIIA 2018 and ITCA 2018. The 8th International Symposium on Computational Intelligence and Industrial Applications and The 12th China-Japan International Workshop on Information Technology and Control Applications, 02-06 Nov 2018, Tengzhou, China. This is an author produced version of a paper presented at ISCIIA 2018 and ITCA 2018. Reuse Items deposited in White Rose Research Online are protected by copyright, with all rights reserved unless indicated otherwise. They may be downloaded and/or printed for private study, or other acts as permitted by national copyright laws. The publisher or other rights holders may allow further reproduction and re-use of the full text version. This is indicated by the licence information on the White Rose Research Online record for the item. Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing [email protected] including the URL of the record and the reason for the withdrawal request. [email protected] https://eprints.whiterose.ac.uk/ Introduction to Modeling and Simulation Techniques Introduction to Modeling and Simulation Techniques Chenggang Yin*1 and Alison McKay2 *1 Department of Mechanical Design and Manufacture, College of Engineering, China Agricultural University, Beijing, China E-mail: [email protected] 2 School of Mechanical Engineering, University of Leeds, Leeds, UK E-mail: [email protected] Abstract: Modeling and simulation techniques are simulation (DES) method [5], and Macal and North becoming an important research method for proposed an agent-based simulation (ABS) tutorial [4]. investigating operational and organizational systems. Siebers et. al. presented advantages and disadvantages Many literatures report different aspects and views of between DES and ABS, Sargent considered different modeling and simulation but there is little literature that approaches for simulation model verification and validation covers a full cycle of modeling and simulation, including [7], Hughes et. al. reported modeling and simulation both model design & development and model applications to organizational systems [8], and Abar et. al. verification & validation, for use in industrial product provided a review of agent-based simulation methods and development systems. This paper introduces modeling development [9]. and simulation concepts, methods and tools, and Many researchers work on modeling and simulation discusses approaches that can be used for model methods, procedures, strategies and applications in different verification and validation. A modeling and simulation scientific research areas. However, there is little literature procedure, designed for use in understanding industrial that covers a full cycle of modeling and simulation, product development systems, is introduced that including both model design & development and model accommodates both model creation and verification & verification & validation, for use in industrial product validation. The overall goal of the research is to bridge development systems. As a result, it can be difficult for the gap between model design & development and model practitioners to determine the validity of given simulation verification & validation in a modeling and simulation models and so the reliability of results from simulation procedure which, as a whole, is essential for the experiments. application of modeling and simulation techniques to This paper introduces a procedure (see Section 5) that understand any real-world system. covers a full cycle of modeling and simulation, including both model design & development and model verification & Keywords: Modeling and Simulation, Modeling and validation, for use in industrial product development Simulation Procedure, Model Verification and systems. The procedure was evaluated through application Validation, Agent-Based Simulation (ABS), Discrete- to a real-world new product development process case study Event Simulation (DES) as part of a PhD research project [48]. The procedure is based on modeling and simulation concepts discussed in Section 2; and modeling and simulation domains and methods that are introduced in Sections 3 and 4 respectively. 1. BACKGROUND Section 6 considers model verification and validation methods in more details and Section 7 concludes the paper. Modeling and simulation techniques are being widely applied in organizational and operational systems, in 2. MODELING AND SIMULATION CONCEPTS addition to their success in physical system design, manufacture, analysis and improvement. Modeling and Two definitions of modeling and simulation were used as simulation involves a process of designing a model of a real- the basis of this work. Modeling and simulation is defined world or anticipated system such as a design concept, then by Bratley et. al. as a process of driving a model of a system conducting experiments with the model for the purposes of with suitable inputs and observing the correspondingly understanding the performance of the system under outputs [10] and by Shannon as the process of designing a different operating conditions and evaluating alternative model of a conceptual system and using it to conduct management strategies and decision-making processes [1, experiments for the purpose of understanding the 2]. Modeling and simulation technology is increasingly performance of the system and/or evaluating alternative considered to be a third scientific research methodology, in management strategies and decision-making processes addition to the traditional deductive and inductive using simulation results [1, 2]. approaches [3, 4]. The purpose of modeling and simulation includes Many researchers have contributed to modeling and performance assessment, proof, prediction, discovery, simulation technologies. For example, Shannon gave a training, entertainment and education [3]. Simulation definition of simulation and predictive modeling [1], techniques are applied in various research fields including Klingstam and Gullander introduced the discrete-event computer systems, manufacturing processes, societal The 8th International Symposium on Computational Intelligence and Industrial Applications (ISCIIA2018) The 12th China-Japan International Workshop on Information Technology and Control Applications (ITCA2018) 1 Binjiang International Hotel, Tengzhou, Shandong, China, Nov. 2-6, 2018 Introduction to Modeling and Simulation Techniques systems, business organizations, government systems, additional time resources for design iteration across ecology environment systems, and other complex processes different stages in the process with a view to identifying and systems [1, 2]. Modeling and simulation methods have improved management strategies, with an overall goal to also been applied to interdisciplinary research fields such as shorten product development duration and so improve design system decision-making mechanisms [11, 12], the time-to-market performance. The focus of the rest of this management of integrated product teams [13], new product paper is on process and system modeling and simulation. development processes [14, 15, 16], and organizational management [8]. Application of modeling and simulation methods to understand the performance of complex socio- technical systems is becoming a promising research area [3, 8]. 3. MODELING AND SIMULATION DOMAINS In engineering, modeling and simulation techniques are applied to two distinct types of system: physical Fig. 2 Process and system simulation mechanisms whose performance is governed by the laws of physics and process-based systems whose performance are governed by human, group and organizational behaviors. 4. MODELING AND SIMULATION METHODS 3.1. Mechanism Simulation Two common simulation methods applied in operational management systems are agent-based simulation (ABS) [6] Mechanism simulation relates to the simulation of physical and discrete-event simulation (DES) [22]. These can be used systems, through which movement, degree of freedoms in conjunction with other simulation methods such as (DOFs), velocities and component stresses can be simulated mathematical simulation and Monte Carlo simulation. and analyzed for whole machine optimization. Fig. 1 displays an example kinematic simulation of 3D CAD 4.1. Agent-Based Simulation (ABS) model. Agent-based simulation (ABS) is a fast-developing modeling and simulation method [9, 23, 24, 25] that can be used to model and simulate industrial process and complex scientific systems [26, 27]. Agent-based simulation builds up its models using a bottom-up architecture [4, 23]. It comprises a series of autonomous agents that act and interact with each other complying with defined simulation specifications in a simulation world. Key characteristics of agent-based simulation are as follows [15, 23]: • bottom-up modeling architecture; • focus on modeling individual agents and interactions between them; Fig. 1 Mechanism simulation • a decentralized simulation model architecture, i.e., each agent has its own thread of control; Fig. 1 shows an assembly of a spatial linkage mechanism • The modeled system performance is not defined in the that includes different mechanical