Etapas de un modelo de simulacion

Nine Steps to A Successful Simulation Study

Author: Cliff King

1. Define the problem
2. Formulate an objective
3. Describe the system and list any assumptions
4. List possible alternative solutions
5. Collect data and gather information
6. Build the computer model
7. Verify and validate the model
8. Run alternative experiments
9. Analyze outputs

These nine steps are brieflydefined below. It is not intended to be a comprehensive discussion, but merely a general guide. Remember that a simulation study is not a simple sequence of steps. Some projects may require going back to previous steps as more insight into the system is obtained. The steps of verification and validation will be part of each step of the project.

1. Define the problem

A model that representsall aspects of reality for your whole system is impossible or, at best, too expensive. Besides, such a model is often a bad one. It will be far too complex and hard to understand. Therefore, it is advisable to first define a problem, formulate an objective and then build a model that is 100% designed to solving the problem. Care must be taken to not make an erroneous assumption when defining theproblem. For instance, rather than state that there are not enough receiving docks, state that truck waiting time is too long. As a guideline, formulate a problem statement as generally as possible, think of possible causes for the problem and then, if possible, define the problem more specifically.

2. Formulate an objective and define the system performance measures

A simulation studywithout an objective is useless. The objective is meant to be a guide through each step of the project. The description of the system is defined with the objective in mind. The objective determines what assumptions can be made. What information and data needs to be collected depends upon the objective. The model is built and validated to specifically meet the objective. And of course, theoutput results collected are done so with the purpose of satisfying the objectives. The objective needs to be clear, unambiguous, and feasible. Objectives can often be expressed as questions such as “Is it more profitable to increase capacity by adding machinery or by working overtime?” When defining the objective, it is necessary to specify the performance measures that will be used to determine ifthe objective is met. Hourly production rate, operator utilization, average queuing times, and max queue size are typical performance measures.

Finally, list any preconditions for the simulation results. For example, the objective must be realized using the existing facility, or the maximum investment amount must not be exceeded, or the product lead-time can not increase.

3. Describe themodel and list any assumptions

Simply stated, a simulation model captures the time it takes to do things. The times in a system are split up between process times, transportation times, and queuing times. Whether the model is a logistics system, a manufacturing plant, or a service operation, it is necessary to clearly define the following modeling elements: resources, flow items (products,customers or information), routings, item transformations, flow control, process times, and resource down times. Here’s a brief description of each.

There are four basic types of resources: processors, queues, transports, and shared resources such as operators. The arrival and preloading requirements of the flow items must be defined in terms of arrival times, arrival patterns and types of items.When defining flow routes, detailed descriptions are required for merges and diverts. Item transformations include attribute changes, assembly operations (items combining), and disassembly operations (items splitting). Often there will be the need for controlling the flow of items in the model. For instance, an item may be forced to stop until a condition or time is met, and then released...