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

仿真优化方法和建模以生成不确定性下规划的替代方案

• 批准号: RGPIN-2015-04916
• 负责人: Yeomans, Julian
• 金额: $ 1.46万
• 依托单位: York University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=590106
• 关键词: Simulation; Optimization; Methods; Modelling; Generate

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.

当其众多的系统组件表现出相当程度的不确定性时，综合规划问题被证明是特别复杂的。这种复杂性经常被许多不能事先确定的随机因素所加剧。通常不存在精确的分析公式，即使它们存在，也包含大量高度随机、非线性的成分。为这样的大型随机问题确定好的解决方案，同时包含固有的不确定性，可能会被证明是极其困难的。不管这些困难如何，这种规划必须在“现实世界”决策、规划和政策制定的几乎每一个领域中进行。模拟优化(SO)提供了一种包含以概率分布表示的不确定性的优化方法，用于构造复杂规划问题的最优解。因此，所有未知的目标(S)、约束和参数都被模拟模型取代，在模拟模型中，决策变量提供了运行每个模拟实验的设置。有效的优化组件引导通过可行域的解探索，仅执行有限数量的模拟。虽然我以前的一些研究表明，SO可以在大规模规划应用中产生令人印象深刻的结果，但该过程的解决时间/质量可能因实施而异，从而限制了其对所有规划目的的普遍适用性。将寻求改进搜索程序。我的研究将探索各种方法，通过减少求解时间和/或为上述大规模决策和规划问题产生更高质量的解决方案来提高SO的性能。