In industry, managers are constantly faced with situations requiring making decisions that can have important consequences for profitability, market share, customer satisfaction, and other key performance metrics. Given the significant complexity and uncertainty inherit to most such decisions, managers require an effective tool to test a variety of real-world decision-making scenarios. Computer simulation has evolved as the methodology of choice to meet this need.
In this paper, we propose an approach that incorporates an advanced data mining module to identify relevant system inputs and analyze the way these inputs interact within the system. This dynamic data mining procedure is activated during the optimization process in order to make use of information obtained therein, with the goal of speeding the search for optimal solutions. In addition, our recommended approach incorporates the state-of-the-art OptQuest optimization engine to guide the search for optimal polices or scenarios for a given system based on user-defined performance measures. We include an example of a start-up firm seeking venture capital financing to illustrate the use and power of this methodology.
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