Collaborative Research: Inference, Analysis and Assessment in Simulation Optimization

该补助金提出了模拟优化（SO）问题的三个主要工作领域，这些问题是目标函数和约束条件只能通过随机模拟来观察的优化问题。首先，许多SO问题具有结构，如凸性或单峰性，如果检测到，可以改善？的理解的问题本身，并利用在选择解决方案的算法。将开发数值方法来检测这种结构。其次，性能指标，并有效地计算它们的方法，将开发，使理论上健全的SO算法的性能测试问题的比较。第三，将开发一个SO问题的测试平台。如果获奖，数字检测问题结构的能力将大大提高对一个？的问题公式，并允许更多地使用专门的算法，利用结构。这也可能导致用户制定的问题，坚持这些结构，其结果是，许多新的子类SO问题可能会被创建。测试平台，沿着适当的性能测量，应该有助于鼓励算法的比较和开发。到那时，我们也许能够解决比今天可能解决的更大的SO问题。 研究结果将在应急服务、运输物流、供应链管理、收入管理等领域得到应用。

This 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.