Modeling and Simulation: Exploring Dynamic System Behaviour

1. Modeling and Simulation:Exploring Dynamic System Behaviour Chapter 1 Introduction

2. Opening Perspectives • Modelling and simulation project as a problem solving tool • Dynamic System context • Problem needs to be solved • System under investigation (SUI) • Interested in behaviour over time (dynamic system) • Viewed as collection if interacting entities to produce some behaviour over an interval of time • Broad range of systems: • Physical systems such as transportation systems and manufacturing systems • Less tangible systems such as healthcare systems, social and economic systems • Model • Representation or abstraction of the SUI (e.g. mathematical models) • Must be appropriate to problem being solved • Simulation Experiments • Emergence and widespread availability of computer power that makes it possible for experimenting with complex models

3. Conceptual Model and Simulation Model • Conceptual Model: The art of representing a real-world system using some type of formalism (mathematic/symbolic in nature). • Way of capturing, building and sharing a knowledge-base about the real-world system • Specification provides the means to express the model using some well understood “language” • Simulation Model: Representing the model as a computer program that can be executed • The simulation model can usually be executed using a number of simulation techniques (using general programming language, simulation software, etc.)

4. Role of Modelling and Simulation • Searching “Computer Simulation” in University library database (ORBIS) returns 370 subjects (663 books) • Science: Biology, Physics, Chemistry, Electricity, Quantum theory • Health/Medicine: health care, cardiovascular systems, brain, cognitive science, genetics, nervous system • Human studies: Psychology, anthropology, crime, automobile driving, evolution, dynamics of welfare, philosophy, government, social science • Engineering: Digital circuits, networking, bridge design, artificial intelligence, traffic engineering • Business/Manufacturing: Economics, corporate finance, decision making, Emergency management, industrial management, marketing, operations science, risk management • The world around us: ecology, climate, demographics, forest ecology and management, geology, pollution • Training: Virtual reality, education • For fun: Game theory, virtual reality, robot soccer

5. Why do we model and simulate? • Too costly • Too dangerous • Too time consuming • Too disruptive • Morally or ethically unacceptable • Irreversible

6. The Nature of a Model • Specification for behaviour generation • Level of granularity appropriate for the problem at hand, i.e. to meet the goals of the study • “Everything should be made as simple as possible, but not simpler”, Albert Einstein • When is a model good enough? • Good enough from the point of view of the project goal • Methods for modelling • Natural language • Mathematical formalisms • Rule-based formalisms • Symbolic/graphical representations • Any combination of the above

7. Types of models • Modeling missiles • Models can be exact (physical model) to very abstract (digital model) • Course focuses on creating models for digital execution

8. Full Service Gas Station • Two islands and four service lanes • Depending on time of day one or two attendants serve customers • A significant portion of customers drive vans and light trucks • Large tanks take more time to fill • Drivers of passenger cars wait longer for service behind the vans or light trucks – leads to complaints

9. Full Service Gas Station • Servicing a customer has three phases • During the first phase, the attendant determines customer’s requirements and starts pumping the gas, cleans windshield, checks fuel levels, etc. • Delivery phase during which gas is pumped (attendant need not be present) • The payment phase when the attendant receives payment by cash or credit card • Management is considering restricting the vans and light trucks to 2 of the lanes to improve the flow of vehicles

10. Full Service Gas Station • Description presented can be regarded as an initial phase of a M&S study. • What details may be missing from the description of the gas station problem? • Are there any refinements to be made?

11. Challenges to Modelling and Simulation • Inappropriate statement of goals • Inappropriate granularity of a model • Ignoring unexpected behaviour • Inappropriate mix of essential skills • Inadequate flow of information to the client

12. Monte Carlo Simulation and Simulators • Monte Carlo Simulation • A family of techniques to solving numerical problems • Construct a stochastic (probabilistic) system for solving underlying problem • Method exploited by von Neumann and colleagues to solve problems in developing a nuclear arsenal in latter years of World War II. • Simulators • Training platforms for educating operators (eg. Flight simulator) • Real-time operation of the simulated system behaviour

13. Historical Overview • 1920’s – Edward Link creates the Link Trainer, a flight simulator, using pneumatic/hydraulic technology • 1950’s – Analog computers are commercially available and used for creating the first computer simulations. • 1960’s – The digital computer offers an alternative to the analog computer and provides a means for simulating discrete event systems incorporating stochastic phenomena • 1970’s and 1980’s – Numerous modeling and simulation applications are developed. • Provides the means to undertake larger project that typically fall under one of two realms: continuous systems and discrete event systems