Collaborative Research: Inference, Analysis, and Assessment in Simulation Optimization

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Collaborative Research: Inference, Analysis, and Assessment in Simulation OptimizationAbstractThis grant proposes three main areas of work on simulation optimization (SO) problems, which are optimization problems where the objective function and constraints involved can only be observed through a stochastic simulation. First, many SO problems possess structure such as convexity or unimodality that, if detected, can improve one?s understanding of the problem itself, and be exploited in selecting solution algorithms. Numerical methods will be developed to detect such structure. Second, performance measures, and methods for efficiently computing them, will be developed to enable theoretically sound comparisons of the performance of SO algorithms on test problems. Third, a testbed of SO problems will be developed.If awarded, the ability to numerically detect problem structure will greatly improve understanding of one?s problem formulations, and allow greater use of specialized algorithms that exploit structure. This could also lead to users formulating problems to adhere to those structures, with the result that many new subclasses of SO problems might be created. The testbed, along with appropriate performance measures, should help to encourage algorithm comparisons and development. We might then be able to tackle far larger SO problems than is possible today. The results of the research will find application in areas such as emergency services, transportation logistics, supply chain management, revenue management and potentially many other fields.