Computer Simulation in Management Science, 5th Edition
April 2006, ©2005
PART I: FUNDAMENTALS OF COMPUTER SIMULATION IN MANAGEMENT SCIENCE.
1 The computer simulation approach.
1.1 Models, experiments and computers.
1.2 Some applications of computer simulation.
1.2.2 Health care.
1.2.3 Business process re-engineering.
1.2.4 Transport systems.
1.3 Models in management science.
1.4 Simulation as experimentation.
1.5 Why simulate?
1.5.1 Simulation versus direct experimentation.
1.5.2 Simulation versus mathematical modelling.
2 A variety of modelling approaches.
2.1 General considerations.
2.2 Time handling.
2.2.1 Time slicing.
2.2.2 Next-event technique.
2.2.3 Time slicing or next event?
2.3 Stochastic or deterministic?
2.3.1 Deterministic simulation: a time-slicing example.
2.3.2 Stochastic simulation.
2.4 Discrete or continuous change.
2.4.1 Discrete change.
2.4.2 Continuous change.
2.4.3 A few words on simulation software.
3 Computer simulation in practice.
3.1 Process, content, problem and project.
3.1.1 Process and content.
3.1.2 Problems and projects.
3.1.3 Two parallel streams.
3.2 The simulation problem part of the study.
3.3 Problem structuring.
3.3.1 Problem structuring as exploration.
3.4.1 Conceptual model building.
3.4.2 Computer implementation.
3.5 The project part of the study.
3.5.1 Initial negotiation and project definition.
3.5.2 Project management and control.
3.5.3 Project completion.
4 Static Monte Carlo simulation.
4.1 Basic ideas.
4.1.1 Risk and uncertainty.
4.1.2 The replacement problem: a reprise.
4.1.3 Static Monte Carlo simulation defined.
4.2 Some important considerations.
4.2.1 Subjective probabilities.
4.3 Some simple static simulations.
4.3.1 The loan repayment.
4.3.2 An investment decision.
4.4 Simulation on spreadsheets.
PART II: DISCRETE EVENT SIMULATION.
5 Discrete event modelling.
5.2.1 Objects of the system.
5.2.2 The organization of entities.
5.2.3 Operations of the entities.
5.3 Activity cycle diagrams.
5.3.1 Example 1: a simple job shop.
5.3.2 Example 2: the harassed booking clerk.
5.3.3 Example 3: the delivery depot.
5.3.4 Using the activity cycle diagram.
5.4 Activity cycle diagrams: a caveat.
6 How discrete simulation software works.
6.1.1 Why understand how simulation software is organized?
6.1.2 Simulation executives in more detail.
6.1.3 Application logic.
6.2 The three-phase approach.
6.2.3 The exception to the general rule.
6.2.4 Bs and Cs in the harassed booking clerk problem.
6.2.5 Another example: a T-junction.
6.3 How the three-phase approach works.
6.3.1 The A phase.
6.3.2 The B phase.
6.3.3 The C phase.
6.4 The harassed booking clerk—a manual three-phase simulation.
6.4.1 The first A phase.
6.4.2 The first B phase.
6.4.3 The first C phase.
6.4.4 The second A phase.
6.4.5 The next B and C phases.
6.4.6 The third A phase.
6.4.7 The third B phase.
6.5 The event-based worldview.
6.5.1 Events in the harassed booking clerk problem.
6.5.2 Event-based executives.
6.6 The activity-scanning approach.0
6.6.2 Activity-scanning executives.
6.7 Process-based approaches.
6.7.1 Processes in the harassed booking clerk problem.
6.7.2 Process interaction.
6.7.3 Process-based executives.
6.8 Which approach is best?
6.8.1 Three-phase versus process-based approaches.
7 Writing a three-phase simulation program.
7.1.1 The basic structure of the library.
7.2 Inside the executive.
7.2.1 The control array.
7.2.2 Using the control array to operate a three-phase simulation.
7.3 The Visual Basic implementation.
7.3.1 Some comments on Visual Basic.
7.3.2 The variables and their types.
7.3.3 The A phase.
7.3.4 The B phase.
7.3.5 The C phase.
7.3.6 Running the simulation.
7.4 Using VBSim to simulate the harassed booking clerk problem.
7.4.1 Entities, Bs and Cs.
7.4.2 Personal enquirers and phone calls arrive.
7.4.3 The end of personal service and phone calls.
7.4.5 The Cs.
7.4.6 Initialization and finalization.
7.5 Putting it all together.
8 Visual interactive modelling and simulation.
8.1 Basic ideas.
8.1.1 Visual interactive modelling (VIM).
8.1.2 Visual simulation output.
8.1.4 A caveat.
8.2 Designing a visual simulation display.
8.2.1 Iconic displays.
8.2.2 Logical displays.
8.2.3 Chart displays.
8.3.1 Joe’s exhaust parlour.
8.3.2 Joe’s exhaust parlour in Micro Saint: model building.
8.3.3 Joe’s exhaust parlour in Micro Saint: running and analysing the simulation.
8.3.4 Joe’s exhaust parlour in SIMUL8: model building.
8.3.5 Joe’s exhaust parlour in SIMUL8: running and analysing the simulation.
8.4 Visual interactive simulation: a reprise.
9 Discrete simulation software.
9.1 General principals.
9.2 A quick overview of discrete simulation software.
9.3 VIMS and their relatives.
9.3.1 VIMSEa reprise.
9.3.2 Block diagram systems.
9.3.3 VIMS and block diagram systems.
9.4 Programming using a general purpose language.
9.4.1 Pros and cons.
9.4.2 Libraries and component-based software.
9.5 Programming approaches using simulation languages.
9.5.1 Common features of simulation languages.
9.5.2 An example: SIMSCRIPT II.5.
9.6 Layered systems and application templates.
9.6.1 Layered systems.
9.6.2 Application templates.
9.7 Appraising simulation software: some principles.
9.7.1 The type of application.
9.7.2 The expectations for end use.
9.7.3 Knowledge, computing policy and user support.
9.8 Which to choose? Horses for courses.
9.8.2 Simulation languages.
10 Sampling methods.
10.1 Basic ideas.
10.1.1 General principles of random sampling.
10.1.2 Top-hat sampling.
10.1.3 The fundamental random sampling process.
10.1.4 Use of pre-written libraries of algorithms.
10.2 Random number generation.
10.2.1 Truly random numbers.
10.2.2 Pseudo-random numbers.
10.2.3 Congruential generators.
10.2.4 General requirements for these generators.
10.2.5 Multiplicative congruential generators.
10.2.6 Improving on simple congruential generators.
10.2.7 Using inbuilt random number generators.
10.3 Testing random number generators.
10.3.1 Scatter plots.
10.3.2 Auxiliary sequences.
10.3.3 Frequency tests.
10.3.4 Serial test.
10.3.5 Gap test.
10.3.6 Other tests.
10.4 General methods for random sampling from continuous distributions.
10.5 Random sampling algorithms for discrete distributions.
10.5.1 Sampling from histograms.
10.5.2 Implicit inverse transformation.
10.5.3 Discrete rejection—samples from a Poisson distribution.
10.6 Sampling from the normal distribution.
10.6.1 The original Box—Müller method.
10.6.2 Box—Mü ller polar variation.
10.6.3 Sampling from a normal distribution by composition.
10.6.4 A poor way to sample from the normal distribution.
10.7 Deriving one distribution from another—log-normal variates.
10.8 Sampling from non-stationary processes: thinning.
11 Planning and analysing discrete simulation output.
11.1 Fundamental ideas.
11.1.1 Simulation as directed experimentation.
11.1.2 Estimation and comparison.
11.1.3 Three important principles.
11.1.4 Some preliminary advice.
11.2 Dealing with transient effects.
11.2.1 Terminating and non-terminating systems.
11.2.2 Achieving steady state.
11.2.3 Using a run-in period.
11.2.4 Welch’s method for determining the run-in period.
11.3 Dealing with lack of independence.
11.3.1 Simple replication.
11.3.2 Using batch means.
11.3.3 Overlapping batch means (OBM).
11.3.4 Regenerative methods.
11.4 Variance reduction.
11.4.1 The basic problem—sampling variation.
11.4.2 Set and sequence effects.
11.4.3 Common random number streams and synchronization.
11.4.4 Control variates (regression sampling).
11.4.5 Antithetic variates.
11.5 Descriptive sampling.
11.5.1 Basic idea.
11.6.1 Basic ideas.
11.6.2 Factorial experiments.
12 Model Testing and Validation.
12.1 The importance of validation.
12.1.1 Validation is impossible, but desirable.
12.1.2 Some practical issues.
12.1.3 The ‘‘real’’ world, the model and observation.
12.1.4 The hypothetico-deductive approach.
12.1.5 The importance of process and other aspects.
12.2 Validation and comparison.
12.2.1 Experimental frames.
12.2.2 Program verification and model validation.
12.3 Black box validation.
12.3.1 Black box validation: a model’s predictive power.
12.3.2 How valid?
12.3.3 Validation errors.
12.3.4 Testing model components.
12.4 White box validation.
12.4.1 Detailed internal structure.
12.4.2 Input distributions.
12.4.3 Static logic.
12.4.4 Dynamic logic.
12.5 Type zero errors.
12.5.3 Steering a sensible course.
PART III: SYSTEM DYNAMICS.
13 Structure, behaviour, events and Feedback systems.
13.1 Events, behaviours and structures.
13.1.1 System simulation.
13.1.2 The importance of system structure.
13.2 Feedback systems.
13.2.1 Hierarchical feedback systems: an example.
13.2.2 Causal loop diagrams.
13.3 Modelling feedback systems.
13.3.2 Levels and stocks.
13.3.3 Rates and flows.
13.4 The origins of system dynamics.
13.4.1 Control theory.
14 System dynamics modelling and simulation.
14.1.1 Stock and flow diagrams.
14.1.2 A stock and flow diagram for Big Al’s problem.
14.2 Beyond the diagrams—system dynamics simulation.
14.2.1 Time handling in system dynamics.
14.2.2 Equation types.
14.2.3 Powersim equations for Big Al’s problem.
14.2.4 Integration and the value of dt.
14.3 Simulating delays in system dynamics.
14.3.1 Pipeline delays.
14.3.2 Exponential delays.
14.3.3 Information delays.
14.4 System dynamics modelling.
14.4.1 Modelling from the outside in.
14.4.2 Modelling from the inside out.
15 System dynamics in practice.
15.1 Associated Spares Ltd.
15.1.1 The problem as originally posed.
15.1.2 The multi-echelon system.
15.1.3 The retail branch model.
15.1.4 The regional warehouse model.
15.1.5 The central warehouse model.
15.1.6 The total system model.
15.1.7 Some conclusions.
15.1.8 A postscript.
15.2 Dynastat Ltd.
15.2.1 An expansion programme.
15.2.2 The manpower problem.
15.2.5 Some effects of this structure.
15.2.6 Validating the model.
15.2.7 Simulation results.
15.2.8 Predicting length of service.
15.2.9 The value of the exercise to Dynastat.
15.3 System dynamics in practice.
15.3.1 Simple models.
15.3.3 New thinking.
15.3.4 Evolutionary involvement.
- a new chapter on Monte Carlo simulation using spreadsheets
- a new look inside discrete simulation software
- simulation models in Visual Basic, SIMUL8 and Micro Saint
- system dynamics using Powersim
- Computer simluation is a crucial aspect of management science - a new edition of a cornerstone work.
- Mike Pidd is head of Management Science at Lancaster University and a past President of the OR Society - he has taught simulation for over 20 years.
- Fully updated to keep pace with advances in the field, including links to current software and discussion of how simulation can achieve real benefits in organizations.
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