CAREER: Multi-Objective Optimization via Simulation: Theory, Methods, and Parallel Computation

职业：通过仿真进行多目标优化：理论、方法和并行计算

• 批准号: 1554144
• 负责人: Susan Hunter
• 金额: $ 50万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1554144&HistoricalAwards=false
• 关键词: CAREER; Multi; Objective; Optimization; via

This Faculty Early Career Development (CAREER) grant is developing theory, methods, and algorithms for decision-making under uncertainty in complex systems that are modeled using computer-based simulations. The specific focus will be on developing implementable algorithms that identify optimal decisions with respect to multiple performance measures. Such problems arise frequently in a variety of applications including finance, energy, transportation, facility location, supply chain management, telecommunication, and healthcare management. Though widespread, these problems are under-studied, and current solution methods may be slow, imprecise, or inaccurate. Developing methods to solve such problems with provable guarantees on speed, precision, and accuracy will enable decision-makers to make better, timely decisions across a variety of disciplines. Society will benefit from improved systems, characterized by increased efficiency and reduced cost. This project also supports the PI's educational goal of disseminating clear and engaging educational materials at the interface of probability and optimization that recruit, train, and retain the next generation of professionals who make decisions under uncertainty.This research will develop theory, methods, and parallel algorithms for solving multi-objective optimization via simulation problems. Multi-objective optimization via simulation problems are nonlinear multi-objective optimization problems in which each objective can only be observed with error as output from a Monte Carlo simulation; a solution to this problem is a non-dominated (Pareto) set. Despite its prevalence and mature development in the analogous deterministic context, multi-objective optimization via simulation problems have seen relatively little theoretical and algorithmic development in the optimization via simulation literature. These problems are difficult to solve because of their complexity: the objective functions can only be estimated with error through potentially expensive Monte Carlo simulation, and the Pareto set often grows in the number of objectives. The proposed research will develop the theoretical underpinnings of estimating Pareto sets in the stochastic context. Specifically, the proposed theory and methods include scaling for dimension reduction, asymptotic approximation, optimization frameworks that retrieve fast convergence rates, and parallel implementation. Such understanding will lead to new algorithmic methods that evolve optimally in a provable sense and to implementable, efficient parallel algorithms for solving these difficult problems.

该学院的早期职业发展（CAREER）资助正在开发理论、方法和算法，用于在使用计算机模拟进行建模的复杂系统的不确定性下进行决策。具体重点是开发可实现的算法，以识别与多种性能指标相关的最佳决策。此类问题在各种应用中经常出现，包括金融、能源、交通、设施选址、供应链管理、电信和医疗保健管理。尽管这些问题很普遍，但尚未得到充分研究，并且当前的解决方法可能很慢、不精确或不准确。开发解决此类问题的方法，并在速度、精度和准确度上提供可证明的保证，将使决策者能够在各个学科中做出更好、及时的决策。社会将从改进的系统中受益，其特点是提高效率和降低成本。该项目还支持 PI 的教育目标，即在概率和优化的界面上传播清晰且引人入胜的教育材料，以招募、培训和保留在不确定性下做出决策的下一代专业人员。这项研究将开发理论、方法和并行方法。通过模拟问题解决多目标优化的算法。通过模拟问题进行的多目标优化是非线性多目标优化问题，其中每个目标只能以蒙特卡罗模拟的输出作为误差来观察；该问题的解决方案是非支配（帕累托）集。尽管在类似的确定性环境中它很流行并且发展成熟，但通过模拟问题进行多目标优化在通过模拟进行优化的文献中理论和算法的发展相对较少。这些问题由于其复杂性而难以解决：目标函数只能通过可能昂贵的蒙特卡罗模拟来估计误差，而且帕累托集的目标数量通常会增加。拟议的研究将发展在随机背景下估计帕累托集的理论基础。具体来说，所提出的理论和方法包括降维缩放、渐近逼近、检索快速收敛速度的优化框架以及并行实现。这种理解将导致新的算法方法在可证明的意义上优化发展，并产生可实现的、高效的并行算法来解决这些难题。

项目成果

An epsilon-constraint method for integer-ordered bi-objective simulation optimization

• DOI: 10.1109/wsc.2017.8247961
• 发表时间: 2017-12-03
• 期刊: 2017 Winter Simulation Conference (WSC)
• 作者: K. Cooper;S. R. Hunter;K. Nagaraj
• 通讯作者: K. Nagaraj

An Introduction to Multiobjective Simulation Optimization

多目标仿真优化简介

• DOI: 10.1145/3299872
• 发表时间: 2019-01-24
• 期刊: ACM Transactions on Modeling and Computer Simulation (TOMACS)
• 作者: S. R. Hunter;Eric A. Applegate;V. Arora;Bryan Chong;K. Cooper;Oscar Rincón;Carolina Vivas
• 通讯作者: Carolina Vivas

SCORE Allocations for Bi-objective Ranking and Selection

双目标排名和选择的分数分配

• DOI: 10.1145/3158666
• 发表时间: 2018-01-03
• 期刊: ACM Transactions on Modeling and Computer Simulation (TOMACS)
• 作者: Guy Feldman;S. R. Hunter
• 通讯作者: S. R. Hunter