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: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=613250
• 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. 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.

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。 将考虑该程序的许多“现实世界”应用，并将使用广泛的测试来确定SO在不同的规划环境中的适用性(以及同样重要的是，其不适用性)。为了衡量和展示新方法带来的效率改进，我将继续使用早期几个案例研究的数据，这些案例研究在城市固体废物规划、含铅废物产品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发展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。还将寻求几种新颖的SO申请，并将展示其应用的好处。更多的研究将把这种方法(以及它的所有新发现的效率)扩展到包含相当大不确定性的多目标问题情况。这将提供重大的理论和实践进展。