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Course Notes Operations Management Simulation

19s:Simulation

Charles E. Oyibo

Simulation is a descriptive technique in which a model of a process is developed and then experiments are conducted on the model to evaluate its behavior under various conditions. Unlike other models, simulation is not an optimizing technique in that it does not produce a solution per se. It enables decision makers to experiment with decision alternatives using a what if approach.

Steps in the Simulation Process

  1. Identify the problem and set objectives
  2. Develop the simulation model
  3. Test the model to be sure that it reflects the system being studied
  4. Develop one or more experiments (conditions under which the model's behavior will be examined)
  5. Run the simulation and evaluate your results
  6. Repear steps 4 and 5 until [we are] satisfied with the result

Monte Carlo Simulation

Monte Carlo simulation is a probabilitic simulation technique used when a process has a random (chance) component. The basic steps of the process are as follows:

  1. Identify a probability distribution for each random component of the system
  2. Work out an assignment so that intervals of random numbers will correspond to the probability distribution
  3. Obtain the random numbers needed for the study
  4. Interpret the results

Advantages and Limitations of Using Simulations

Among the main advantages of simulations are:

  1. It lends itself to problems that are difficult or impossible to solve mathematically
  2. It permits an analyst to experiment with system behavior while avoiding possible risks inherent in experimenting with the actual system
  3. It compresses time so that managers can quickly discern long-term effects
  4. It can serve as a tool for training decision-makers by building up their experience and understanding of system behavior under a wide range of conditions

The limitations associated with simulations include:

  1. Simulation does not produce an optimum solution; it merely indicates an approximate behavrios for a given set of inputs. There are two reasons for this:
    1. By design, there is inherent randomness (i.e. random numbers) in simulation
    2. Simulations are based on models, and models are only approximations of reality
  2. For large-scale simulation, it can require considerable effort to develop a suitable model as well as considerable computer time to obtain simulations.

Finally, because simulation produces an approximate answer rather than an exact solution, and because of the cost of running a simulation study, simulation is not usually the first choice of a decision maker. Instead, depending on the complexity of the situation, intuitive or analytical methods should first be investigated.

 

 

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Page Last Updated: Sunday December 5, 2004 6:15 PM