9.3.2 – Define Objectives
The objective of your project is the final outcome you want to achieve. SimRunner measures your progress toward this goal using an objective function. An objective function is composed of response statistics, a min/max or target range, and a specific weight you wish to apply to each response statistic.
The response category is the type of statistic you wish to use to evaluate your model. Response categories include model elements such as locations, entities, resources, and variables.
By clicking on one of the Response Categories, you will see a list of Response Statistics. Simply put, response statistics are those values you wish to improve. Once you define your targeted improvements, you are ready to define how you want them to perform—the objective for the response statistic.
Objective for Response Statistic
The objective for the response statistic refers to the way in which you want to effect change for that item. If you are trying to increase the overall output of a system, you would maximize the response statistic. Likewise, you could minimize the statistic or target a specific range within which you want the result. Finally, enter the reward or weight for each response statistic—larger numbers signify greater rewards in situations that require more than one objective.
Max Check this option if you want to maximize the final value of this statistic.
Min Check this option if you want to minimize the final value of this statistic.
Maximized objective functions return positive values. Minimized objective functions return negative values.
Target Range Check this option to enter a specific target range within which you want the final result.
Statistic’s weight Weights serve as a means of load balancing for statistics that might bias the objective function. Since most simulation models produce a variety of large and small values, it is often necessary to weight these values to ensure that the objective function does not unintentionally favor any particular statistic. For example, suppose that a run of your model returns a throughput of .72 and an average WIP of 4.56. If you maximize throughput and minimize WIP by applying the same weight to
both ( W 1 = W 2 ), you will bias the objective function in favor of WIP:
Maximize [(W 1 )*(Throughput)] = .72
Minimize[(W 2 )*(WIP)] = 4.56
In this case, since you want to ensure that both statistics carry equal weight in the objective function, you will apply a weight of 6.33 ( W 1 =6.33) to throughput and 1.0 ( W 2 =1.0) to WIP to make them of equal weight in the objective function.
Maximize[(W 1 )*(Throughput)] = 4.56
Minimize[(W 2 )*(WIP)] = 4.56
In situations where it is necessary to favor one statistic over another, balancing the statistics first will make it easier to control the amount of bias you apply. For example, if you apply a weight of 12.67 ( W 1 =12.67) to throughput and 1.0 ( W 2 =1.0) to WIP, the objective function will consider throughput to be twice as important as WIP (adapted from Harrell, Ghosh, and Bowden 2000).
Typically, you will need to experiment with your model to identify the weight ratio necessary to balance statistics.
Response statistics selected for objective function
After you define the objective for the response statistic, you may click the Add button to include the statistic as part of the objective function. SimRunner combines the statistics into a linear combination and displays the updated objective function for the project.
The objective function is an expression used to quantitatively evaluate a simulation model’s performance. By measuring various performance characteristics and taking into consideration how you weigh them, SimRunner can measure how well your system operates. However, SimRunner knows only what you tell it via the objective function. For instance, if your objective function measures only one variable, Total_Throughput, SimRunner will attempt to optimize that variable. If you do not include an objective function term to tell it that you also want to minimize the total number of operators used, SimRunner will assume that you don’t care how many operators you use. Since the objective function can include multiple terms, be sure to include all of the response statistics about which you are concerned.
SimRunner’s capacity to include many different response statistics in an objective function gives it tremendous capability. For example, the objective function below signifies that you wish to maximize the total ovens and cooktops processed while minimizing the total resource cost. The numeric weighting factors indicate that maximizing Total Ovens Processed is the most important, followed by maximizing Total Cooktops Processed, then minimizing Total Resource Cost.
Z =Max:10 * (Total Ovens Processed) +