Combining Simulation-Decomposition, Simulation-Optimization, and Modelling-to-Generate-Alternatives for Planning Under Uncertainty

结合仿真分解、仿真优化和建模生成替代方案以进行不确定性下的规划

• 批准号: RGPIN-2022-04619
• 负责人: Yeomans, Julian
• 金额: $ 1.89万
• 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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=761859
• 关键词: Combining; Simulation; Decomposition; Optimization; Modelling

Recently, a new exploratory visualization approach, simulation decomposition (SimDec), has been introduced that extends Monte Carlo analysis by enhancing the explanatory power of the cause-effect relationships between multi-variable combinations of inputs and the simulated outputs. SimDec can reveal previously unidentifiable cause-and-effect connections between multi-variable combinations of inputs on the outputs. A SimDec approach is generalizable to any Monte Carlo model with negligible additional computational overhead and, hence, can be readily used in any analyses that employ simulation models. The proposed research will extend the SimDec method with respect to exploratory data approaches, input/output identification, partitioning, scenario creation, and enhanced visualization techniques, and investigate different applications in large-scale planning settings. Furthermore, using variance decomposition, prior Monte Carlo approaches have calculated indices to evaluate the sensitivities of individual parameters and their interactions on outputs. So-called Sobol analysis determines first-order and total-effect sensitivity indices for the contributions of each input parameter to model output. Along these lines, the proposed study will examine ways to create SimDec-based first-, higher-order, and interaction sensitivity indices - both analytically and numerically - that can be applied not only to SimDec applications, but are also extendable to any Monte Carlo model. Simulation-optimization (SO) provides an optimization approach that incorporates uncertainties expressed as probability distributions. In SO all unknown objectives, 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. Of interest is to concatenate the visual analytic facets of SimDec with the optimization features of SO into a novel, hybrid SimDec-SO method and to apply it to modelling-to-generate-alternatives. Numerous "real world" applications of this SimDec-SO approach will be considered and extensive testing will ascertain its applicability in numerous disparate planning contexts. In efforts to gauge and demonstrate the efficiency improvements, 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, extended supply chains, health care, food processing, agriculture, and environmental sustainability - as well as examining other areas of interest. These applications all possess considerable public importance. Most of this research will involve extensive mathematical and computational testing, while certain applications will necessitate on-site visits to other appropriate industrial and application environments.

最近，一种新的探索性可视化方法-模拟分解(SimDEC)被引入，它通过增强输入和模拟输出的多变量组合之间的因果关系的解释能力来扩展蒙特卡罗分析。SimDec可以揭示以前无法识别的输入和输出的多变量组合之间的因果关系。SimDec方法可推广到任何蒙特卡罗模型，而额外的计算开销可以忽略不计，因此，可以很容易地用于任何使用模拟模型的分析。拟议的研究将在探索性数据方法、输入/输出识别、分区、场景创建和增强的可视化技术方面扩展SimDec方法，并调查在大规模规划环境中的不同应用。此外，利用方差分解，以前的蒙特卡罗方法已经计算出指数来评估单个参数及其对输出的交互作用的敏感性。所谓的SOBOL分析确定了每个输入参数对模型输出的贡献的一阶和总效应敏感性指数。沿着这些思路，拟议的研究将研究创建基于SimDec的一阶、更高阶和相互作用敏感性指数的方法-无论是解析还是数值-这些指数不仅可以应用于SimDec应用，而且还可以扩展到任何蒙特卡罗模型。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。因此，所有未知的目标、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。有趣的是，将SimDec的可视化分析方面与SO的优化特性连接在一起，形成了一种新颖的混合SimDec-SO方法，并将其应用于从建模到生成备选方案。将考虑这种SimDec-SO方法的许多“现实世界”应用，并将进行广泛的测试，以确定其在许多不同的规划环境中的适用性。为了衡量和展示效率的提高，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、逆向物流、扩展供应链、医疗保健、食品加工、农业和环境可持续性等不同环境中使用了这种技术，并检查了其他感兴趣的领域。这些申请都具有相当大的公众重要性。这项研究的大部分将涉及广泛的数学和计算测试，而某些应用将需要对其他适当的工业和应用环境进行现场访问。