Descriptive Modelling: Simulation “Simulation is the process of designing a model of a real system and conducting experiments with this model for the purpose either of understanding the behavior of the system or of evaluating various strategies for the operation of the system.” - R.E. Shannon

Characteristics of Simulation A simulation imitates reality- the changing nature of real situations. It describes or predicts the characteristics of a system under different conditions. It is a technique for conducting experiments. The simulation process repeats an experiment many times to get an estimate of the overall effect of certain actions. It is used when a problem is too complex for numerical optimisation techniques.

Workshop Think of some problem areas where you know simulation is used. (At least three).

Simulation Methodology 1.Define problem 2.Construct model 3.Test and validate model 4.Design experiments 5.Conduct experiments 6.Evaluate results 7.Implement solution

Question For each of the simulation areas can you think why simulation is useful.

Advantages of Simulation Theory is straightforward. Time compression can be attained. Descriptive rather than normative. This allows what-if questions. Experimentation can be used to tell what variables are really important. Building a simulation model is a learning process, as the manager is forced to be explicit about every detail of the problem. The model is usually built for one problem. Simulation can handle a wide number of problem types e.g. inventory, staffing, long range planning. It can include the real complexities of problems. It automatically produces many performance measures. It can handle unstructured problems.

Limitations of Simulation Optimal solution can’t be guaranteed, but good ones can be found. Model construction can be slow and costly. Models/solutions are nontransferable. Sometimes it requires special skills.

Types of Simulation Deterministic Running a deterministic simulation twice will give the same results. vs Probabilistic (also called stochastic) – one or more of the independent variables are probabilistic (random) e.g. Monte Carlo method – Discrete distributions – Continuous distributions – Probabilistic simulation via Monte Carlo technique Time Dependent vs Time Independent e.g. production per day vs waiting line problems

Monte Carlo Method The simulation is set up with at least one variable which is random. The value assigned to this variable is given a random value based on a probability function. The simulation is run many times to see the results. Plug-ins exist for Excel which let you do Monte Carlo simulation.

System Dynamics This involves creating simulation models of complex systems to enable us to look at their behaviour. Systems are modelled in terms of stocks and flows and feedback loops.

Assumptions of System Dynamics things are interconnected in complex patterns that the world is made up of stocks, flows, and feedback loops information flows (feedbacks) are intrinsically different from physical flows nonlinear processes and delays are important elements in systems behaviour arises out of system structure.

System Dynamics Population (stock) deaths births R B R= reinforcing feedback loop ( the more people born the more get born..) B= balancing feedback loop – deaths regulate the population

Some Things Systems Simulation Can show us: simple interconnectedness how rational microbehavior can lead to disastrous macroresults: each individual acting in rational self-interest creates a catastrophe for the community as a whole This is one of the most powerful concepts we have to offer, because it turns public discussion from the problem of blame to the problem of restructuring. (Meadows)

Summary Simulation is to experiment with a model.