The research objective of this award is to develop efficient simulation-based optimization methodologies to enable fast-time simulation-based decision making under uncertainty. Simulation and Optimization are two most popular tools in operations research. However, the combination of simulation and optimization is still facing huge efficiency concerns. A decision maker is forced to compromise on simulation accuracy, modeling accuracy, and the optimality of the selected solution. The research in this award intends to seek for a seamless integration of simulation evaluation and optimization search with focus on efficiency. A key contribution involves a trade-off between allocating computational resources for searching the solution space versus conducting additional simulation replications for better estimating the performance of current promising solutions. This research will develop a new Optimized Simulation Optimization (OSO) method which intends to maximize the overall efficiency of simulation optimization.
If successful, the results of this research will provide a set of fast simulation-based optimization methodologies. With such methodologies, a decision maker will be able to model complex, stochastic systems, while obtaining the optimal design very efficiently. The benefit of developing an efficient simulation-based methodology is that it offers unprecedented flexibility to address a wide variety of problems in different application contexts. Example applications include revenue management systems, transportation systems, and manufacturing systems. An open-source simulation optimization software package based on the new methodologies will be developed and be available at a dedicated web site for dissemination purpose. The software will be easily accessible and beneficial to industry practitioners and academic researchers. It will also be usable in several different courses to educate students about simulation-based decision making.
