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Briefly explain the steps in simulation study.

Steps involved in simulation study.

• Below figure shows steps involved in study of simulation.

Steps in simulation are as follows:

1. Problem formulation: Every study should begin with a statement of the problem. If the statement is provided by policymakers, or those that have the problem, the analyst must ensure that the problem being described is clearly understood. If a problem statement is being developed by the analyst, it is important that the policymaker understand and agree with the formulation. Although not shown in the figure there are occasions where the problem must be reformulated as the study progresses. In many instances, policymakers and analysts are aware that there is a problem long before the nature of problem is known.

2. Setting of objectives and overall project plan: The objective indicate the questions to be answered. At this point, a determination should be made concerning whether simulation is the appropriate methodology for the problem as formulated and objectives as stated. Assuming that it is decided that simulation is appropriate, the overall project plan should include a statement of the alternative systems to be considered and of a method for evaluating the effectiveness of these alternatives. It should also include the plans for the study in terms of the number of people involved, the cost of the study, and the number of days required to complete each phase of the work, along with the result expected at the end of each stage.

3. Model conceptualization: The construction of a model of a system is probably as much art as science. The art of modeling is enhanced by an ability to abstract the essential features of a problem, to select and modify basic assumption results. Thus, it is best to start with a simple model and build toward greater complexity. It is not necessary to have a one to one mapping between the model and the real system. Only the essence of real system is needed. It is advisable to involve the model user in model conceptualization. Involving the model user will both enhance the quality of the resulting model and increase the confidence of the model user in the application of the model.

4. Data collection: There is a constant interplay between the construction of the model and the collection of the needed input data. As the complexity of the model changes, the required data elements can also change. Also, since data collection takes such a large portion of the total time required to perform simulation, it is necessary to begin it as early as possible, usually together with the early stages of model building. The objective study dictates, in a large way, the kind of data to be collected. In study of bank, for example, if the desire is to learn about the length of waiting lines as the number of tellers change, the types of data needed would be the distributions of inter-arrival times, the service time distribution for the tellers, and the historic distributions on the lengths of waiting lines under varying conditions. This last type of data will be used to validate the simulation model.

5. Model translation: Most real world systems results in models that require a great deal of information storage and computation, so the model must be entered into a computer recognizable format. We use the word “program” even though it is possible to accomplish the desire result in many instances with little or no actual coding. The modeler must decide whether to program the model in simulation language, such as GPSS/H, or to use special purpose simulation software.

6. Verified: Verification pertains to the computer program prepared for the simulation model. Is the computer program performing properly? With complex models, it is difficult, if not impossible, to translate the model successfully in its entirety without a good deal of debugging; if the input parameters and logical structure of model are correctly represented in the computer, verification has been completed. For the most part, common sense is used in completing this step.

7. Validated: Validation usually is achieved through the calibration of the model, an iterative process of comparing the model against the actual system behaviour and using the discrepancies between the two, and the insight gained, to improve the model. This process is repeated until model accuracy is judged acceptable.

8. Experimental design: The alternatives that must be simulated must be determined. Often, the decisions concerning which alternatives to simulate will be a function of runs that have been completed and analysed. For each system design that is simulated, decisions need to be made concerning the length of the initialisation period, the length of simulation runs, and the replication to be made of each run.

9. Production runs and analysis: Production runs and their subsequent analysis are used to estimate measures of performance for the system designs that are being simulated.

10. More runs: Given the analysis of runs that have been completed, the analyst determines whether additional runs are needed and what design those additional experiments should follow.

11. Documentation and reporting: There are two types of documentation: program and progress. Program document is necessary to understand how program operate. The program modification is much easier. Model users can change parameters if required to determine the relationship between input parameters and output measures of performance, or to determine the relationship between input parameter and output measures of performance. The result of all the analysis should be reported clearly and concisely in a final report. This will enable model users to review the final formulation, the alternative system that were addressed, the criterion by which the alternatives were compared, the results of the experiments, and the recommended solution to the problem.

12. Implementation: The success of implementation phase completely depends upon how well the previous 11 steps are performed. It is also contingent upon how thoroughly the analyst has involved the ultimate model user during the entire simulation process. If the model user has been thoroughly involved during the model building process and if the model user understands the nature of the model and its output, likelihood of a vigorous implementation is enhanced.

                                   Figure: steps in simulation study

The Basic Steps of a Simulation Study The application of simulation involves specific steps in order for the simulation study to be successful. Regardless of the type of problem and the objective of the study, the process by which the simulation is performed remains constant. The following briefly describes the basic steps in the simulation process

Problem Definition The initial step involves defining the goals of the study and determing what needs to be solved. The problem is further defined through objective observations of the process to be studied. Care should be taken to determine if simulation is the appropriate tool for the problem under investigation.

Project Planning The tasks for completing the project are broken down into work packages with a responsible party assigned to each package. Milestones are indicated for tracking progress. This schedule is necessary to determine if sufficient time and resources are available for completion.

System Definition This step involves identifying the system components to be modeled and the preformance measures to be analyzed. Often the system is very complex, thus defining the system requires an experienced simulator who can find the appropriate level of detail and flexibility.

Model Formulation Understanding how the actual system behaves and determining the basic requirements of the model are necessary in developing the right model. Creating a flow chart of how the system operates facilitates the understanding of what variables are involved and how these variables interact.

Input Data Collection & Analysis After formulating the model, the type of data to collect is determined. New data is collected and/or existing data is gathered. Data is fitted to theoretical distributions. For example, the arrival rate of a specific part to the manufacturing plant may follow a normal distribution curve.

Model Translation The model is translated into programming language. Choices range from general purpose languages such as fortran or simulation programs such as Arena.

Verification & Validation Verification is the process of ensuring that the model behaves as intended, usually by debugging or through animation. Verification is necessary but not sufficient for validation, that is a model may be verified but not valid. Validation ensures that no significant difference exists between the model and the real system and that the model reflects reality. Validation can be achieved through statistical analysis. Additionally, face validity may be obtained by having the model reviewed and supported by an expert.

Experimentation & Analysis Experimentation involves developing the alternative model(s), executing the simulation runs, and statistically comparing the alternative(s) system performance with that of the real system.

Documentation & Implementation Documentation consists of the written report and/or presentation. The results and implications of the study are discussed. The best course of action is identified, recommended, and justified.