Week 1 Chapter 1 Introduction to Simulation

CSC401 Simulation Week 1 – Chapter 1 – Introduction to Simulation

A simulation is the imitation of the operation of a real-world process or system over time. Simulation involves the generation of an artificial history of a system and the observation of that artificial history to draw inferences concerning the operating characteristics of the real system.

Develop a system model Set of assumptions concerning the operation of the system. Mathematical, logical, symbolic relationships between the entities (objects of interest) of the system.

Validated system can be used to investigate what-if questions about the real-world system such as Analysis tool to predict the effect of system changes on performance Design tool to predict the performance of new systems

Sometimes a model can be done analytically via mathematical techniques. Many systems are too complex for mathematical solutions and require computer-based simulation.

1.1 When simulation is the appropriate tool.

Study and experimentation of internal interactions of a complex system Informational, organizational, environmental changes can simulated and there effect on the model’s behavior observed. Knowledge gained during design of simulation model is valuable. Verify analytic solutions. Simulation models for training. Animation facilitates visualization.

1.2 When simulation is not appropriate Banks and Gibson in 1997 gave ten rules for evaluating when simulation is not appropriate. 1. and 2. Problem can be solved easily analytically or by common sense. 3. Don’t use if easier to perform direct experiments 4. Don’t use if costs exceed savings. 5. and 6. Don’t use simulation if resources or time are not available. 7. If no data is available, not even estimates, simulation is not advised. 8. Don’t use if not enough time or personnel to validate model. 9. Simulation tough if managers have unreasonable expectations 10. If system behavior too complex or undefined, simulation not appropriate. (Human behavior) 1.3 Advantages and disadvantages or simulation

Simulation mimics what happens in real life. Simulation model does not need to make dubious assumptions. Output from model directly correspond to outputs from real system.

Advantages: 1. New policies and procedures can be done without disruption to real system. 2. New hardware or physical designs can be done before committing resources for acquisition. 3. Hypotheses can be tested for feasibility. 4. Time can be compressed or expanded for a speed-up or slow-down of phenomena. 5. Insight can be obtained about interaction of variables. 6. Insight can be obtained about importance of variables to performance of the system. 7. Bottleneck analysis can be performed to discover where work in progress are being delayed excessively. 8. Simulation study aids in understanding of how the system operates. 9. “What if” questions can be answered.

Disadvantages / offset 1. Model building requires special training. Art, science, experience required. However vendors of simulation software may take all the pain out of development. 2. Simulation results can be difficult to interpret. Interrelationship of variables or randomness of observations? However simulation software vendors have developed output-analysis capabilities within their packages for performing very thorough analysis. 3. Simulation modeling and analysis can be time consuming and expensive. However simulation can be performed faster today than yesterday and will be faster tomorrow. 4. Simulation is used when an analytical solution is possible, or even preferable. Closed-form queuing models are available. However closed-form queuing models are not able to analyze most of the complex systems that are encountered in practice from experience of the authors in consulting.

1.4 Areas of application Winter Simulation Conference is good place to learn about applications and theory. Manufacturing applications Shared resource capacity analysis in biotech manufactoring Semiconductor manufacturing Making optimal design decisions for next-generation dispensing tools Construction engineering and project management Building a virtual shop model for steel fabrication Military applications Specifying the behavior or computer-generated forces without programming Logistics, supply chain, and distribution applications Semiconductor supply-network simulation Transportation modes and traffic Simulation of freeway merging and diverging behavior Business process simulation Simulation’s role in baggage screening in airports Health care A simulation-integer-linear-programming-based tool for scheduling emergency room staff

Simulation for risk analysis is growing: options pricing and insurance. Simulation of large-scale systems, such as the internet backbone and wireless networks, are growing as hardware and software increase their capability to handle large numbers of entities in a reasonable time. Simulation models of automated material handling systems

1.5 Systems and system environment A system is defined as a group of objects that are joined together in some regular interaction or interdependence toward accomplishment of some purpose.

A system is often affected by changes occurring outside the system. Such changes are said to occur in the system environment. In modeling systems, it is necessary to decide on the boundary between the system and its environment. This decision may depend on the purpose of the study. Setting of interest rate may be a constraint or may be an activity of the system.

1.6 Components of a system Entity – object of interest in the system Attribute – property of an entity Activity – represents a time period of specified length Collection of entities that compose a system for one study may be only a subset of the overall system State of a system – that collection of variables necessary to describe the system at any time, relative to the objectives of the study Event – an instantaneous occurrence that might change the state of the system. Endogenous – activities and events occurring within a system Exogenous – activities and events in the environment that affect the system.

Table 1.1 1.7 Discrete and continuous systems Although few systems are wholly discrete or continuous, usually one type of change usually predominates for most systems A discrete system is one in which the sate variables change only at discrete set of points in time. A continuous system is one in which the state variables change continuously over time.

1.8 Model of a system A model is defined as a representation of a system for the purpose of studying the system. Usually only need those aspects of the system that affect the problem under investigation. Chapter 3

1.9 Types of models Models can be classified as being mathematical or physical Mathematical models use symbolic notation and mathematical equations to represent a system A simulation model is a particular type of mathematical model A static simulation model, some time called a Monte Carlo simulation, represents a particular point in time. Dynamic simulation models represent systems as they change over time. Simulation models that contain no random variables are classified as deterministic. Have a known set of inputs which will result in a unique set of outputs A stochastic simulation model has one or more random variables as inputs. Random inputs lead to random outputs. Since outputs are random, they can be considered only as estimates of the true characteristics of a model In a stochastic simulation, the output measures must be treated as statistical estimates of the true characteristics of the system. Discrete models can be used to model a continuous system. Models in text are discrete, dynamic, and stochastic

1.10 Discrete-event systems simulation Discrete –event systems simulation is the modeling of systems in which the state variables changes only at a discrete set of points in time The simulation models are analyzed by numerical methods rather than by analytical methods Analytical methods employ the deductive reasoning of mathematics to “solve” the model. Numerical methods employ computational procedures to “solve” mathematical models. Simulation models which employ numerical models are run rather than solved. Artificial history of the system generated from model assumptions and observations are collected to be analyzed and to estimate the true system performance measures. Large amount of data usually requires the aid of a computer

1.11 Steps in a simulation study Figure 1.3 Problem formulation Setting of objectives and overall project plan The objectives indicate the questions to be answered by simulation Model conceptualization Art and science Art of modeling enhance by an ability to abstact the essential features of a problem. Start simple and build to more complex Involve the model user Data collection Interplay between construction of the model and collection of needed data The objectives of the study dictate the kind of data to be collected Model translation Program the model Use a simulation language such as Extend Verified? Verify the computer program prepared for the simulation model to insure it performs properly Validated? Validation usually is achieved through calibration of the model, an iterative process of comparing the model against actual system behavior and using the discrepancies between the two, and the insights gained, to improve the model. Experimental design The alternatives that are to be simulated must be determined. The decision concerning which alternatives to simulate will be a function of runs that have been completed and analyzed. For each system design, decisions need to be made concerning the length of initialization period, the length of the simulation runs, and the number of replications to be made of each run. Production runs and analysis Production runs, and their subsequent analysis, are used to estimate measures of performance for system designs that are being simulated. Chapters 11 and 12 More runs? Given the analysis of runs that have been completed, additional runs may be needed and may require a different experimental design. Documentation and reporting Program documentation Progress documentation important to keep project on course Better to work with many intermediate milestones with deliverables than with one absolute deadline. Implementation Depends on how well previous steps have been performed Model and underlying assumptions properly communicated important

Phase 1: problem formulation, and setting of objectives and overall design Phase 2: model conceptualization, data collection, model translation, verification, and validation Phase 3: running the model, experimental design, production runs and analysis, additional runs Phase 4: documentation and reporting, and implementation

Problems:

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