Simulation-Optimization Methods and Modelling-to-Generate Alternatives for Planning Under Uncertainty

综合规划问题证明特别复杂时，其众多的系统组件表现出相当程度的不确定性。这种复杂性经常被许多无法事先确定的随机因素所加剧。通常精确的分析公式并不存在，即使存在，也包含大量高度随机的非线性成分。确定好的解决方案，这样大的随机问题，同时纳入固有的不确定性，可以证明是非常困难的。不管这些困难如何，这种规划必须在“真实的世界”决策、规划和政策制定的几乎每一个领域中进行。仿真优化（SO）提供了一种优化方法，它将不确定性表示为概率分布，以构建复杂规划问题的最佳解决方案。在SO中，所有未知的目标、约束和参数都被仿真模型所取代，在仿真模型中，决策变量提供了每个仿真实验运行的设置。高效的优化组件通过可行域引导解决方案探索，仅执行有限数量的模拟。虽然我以前的一些研究已经证明，SO可以产生令人印象深刻的结果，在大规模的规划应用，解决方案的时间/质量的程序可以有很大的不同，从一个实施到另一个-从而限制了它的普遍适用性，为所有的规划目的。将设法改进搜索程序。我的研究将调查各种方法来提高SO的性能，通过减少解决方案的时间和/或产生更好的质量解决方案，为上述大规模的决策和规划问题。 许多“真实的世界”的应用程序将被考虑和广泛的测试将被用来确定SO的适用性（和，同样重要的是，它的不适用性）在不同的规划环境。为了衡量和展示新方法的效率改进，我将继续使用来自几个早期案例研究的数据，这些案例研究在城市固体废物规划、含铅废物的逆向物流和再制造、扩展供应链的规划和管理以及环境可持续性等不同环境中使用了这种技术。这些应用都具有相当大的公共和环境重要性。SO的几个新的应用也将寻求其应用的好处将被证明。进一步的研究将扩展该方法（及其所有新发现的效率）到包含相当大的不确定性的多目标问题的情况。这将带来重大的理论和实践进步。

Comprehensive planning problems prove particularly complicated when their numerous system components exhibit considerable degrees of uncertainty. This complexity is frequently compounded by numerous stochastic elements that cannot be definitively ascertained beforehand. Often precise analytical formulations do not exist and, even when they do, contain a multitude of highly stochastic, non-linear components. Determining good solutions to such large stochastic problems, while simultaneously incorporating the inherent uncertainties, can prove exceedingly difficult. Irrespective of such difficulties, this planning must be performed in virtually every sphere of "real world" decision-making, planning, and policy-formulation. Simulation-optimization (SO) provides an optimization approach that incorporates uncertainties expressed as probability distributions for constructing best solutions to complex planning problems. In SO all unknown objective(s), constraints, and parameters are replaced by simulation models in which the decision variables provide the settings under which each simulation experiment is run. An efficient optimization component guides the solution exploration through the feasible domain performing only a limited number of simulations. While some of my previous research has demonstrated that SO can produce impressive results in large-scale planning applications, the solution time/quality of the procedure can vary considerably from one implementation to another - thereby limiting its universal applicability for all planning purposes. Improved search procedures will be sought. My research will investigate a variety of approaches for improving the performance of SO by decreasing the solution time and/or producing better quality solutions for the aforementioned large-scale decision making and planning problems. Numerous "real world" applications of the procedure will be considered and extensive testing will be employed to ascertain SO's suitability (and, just as importantly, its non-suitability) in disparate planning environments. In efforts to gauge and demonstrate the efficiency improvements from the new approaches, I will continue with data from several earlier case studies that have used this technique in such diverse settings as municipal solid waste planning, reverse logistics & remanufacturing of leaded waste products, planning & management of the extended supply chain, and environmental sustainability. These applications all possess considerable public and environmental importance. Several novel applications for SO will also be sought and the benefits of its application will be demonstrated. Additional research will extend the method (and all its newly-found efficiencies) into multi-objective problem situations that contain considerable uncertainty. This will provide significant theoretical and practical advances.