Large-scale discrete optimization: theory and future vehicle routing applications

大规模离散优化：理论和未来车辆路径应用

• 批准号: 355398-2011
• 负责人: MitrovicMinic, Snezana
• 金额: $ 1.38万
• 依托单位: Simon Fraser 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=572567
• 关键词: Large; scale; discrete; optimization; theory

What does the following management problems have in common: a) Deciding in which order a futuristic mobile space gas station would visit satellites, b) Scheduling surgeries, c) Making environmentally-friendly decisions on the medical lab sample transportation that meets the deadlines? In mathematics and operations research, they are known as discrete optimization problems. Discrete optimization problems arise in all areas of industry, public services, and government, and luckily there exists a large body of related research with significant achievements. However, these practical problems can be hard, dynamic, and of large-scale, and not always solvable in a reasonable time. My research program proposes improvements of theoretical knowledge and advancements to approaches for solving large-scale discrete optimization problems, in static and dynamic settings. The main topics are: (1) model-based hybrid metaheursitics, (2) time-constrained routing, and (3) future vehicle routing applications related to climate change and space industry. The program objectives are: i) design of metaheuristics that use general-purpose integer program solvers for neighborhood searches; ii) exploration of objective functions specifically tailored to dynamic problems; iii) improvement of pickup and delivery services by transshipment; iv) characterization of hard-to-solve instances of time-constrained routing problems; v) exploration of graphs for modeling problems with time and space dimensions; and vi) modeling complex real-world application with large number of technical constraints. The proposed topics represent continuation of my previous research whose results have been published in top scientific journals. Since this proposal tackles real-world applications related to transportation, space industry, and environment, the research results will have impact on our modern-day society. For example, appropriately solving non-emergency transportation in the public services may increase Canada's chances of reducing greenhouse gas emission to reach the Kyoto agreement targets. My research program will contribute to the advanced training of HQP, by having them pursue challenging theoretical research and solve complex practical problems.

以下管理问题有什么共同点：a) 决定未来移动太空加油站访问卫星的顺序，b) 安排手术，c) 在按时完成医学实验室样本运输方面做出环保决策？在数学和运筹学中，它们被称为离散优化问题。离散优化问题存在于工业、公共服务和政府的各个领域，幸运的是，存在大量相关研究并取得了重大成果。然而，这些实际问题可能是困难的、动态的、大规模的，并且并不总是能够在合理的时间内解决。我的研究计划提出了理论知识的改进和在静态和动态环境中解决大规模离散优化问题的方法的进步。主要主题是：（1）基于模型的混合元启发法，（2）时间约束的路由，以及（3）与气候变化和航天工业相关的未来车辆路由应用。程序目标是： i) 设计元启发法，使用通用整数程序求解器进行邻域搜索； ii）探索专门针对动态问题的目标函数； iii) 通过转运改善取货和送货服务； iv) 难以解决的时间受限路由问题实例的特征描述； v) 探索用于时间和空间维度问题建模的图； vi) 对具有大量技术限制的复杂现实应用进行建模。拟议的主题代表了我之前研究的延续，其结果已发表在顶级科学期刊上。由于该提案解决了与交通、航天工业和环境相关的现实世界应用，因此研究成果将对我们的现代社会产生影响。例如，适当解决公共服务中的非紧急交通问题可能会增加加拿大减少温室气体排放以达到京都协议目标的机会。我的研究项目将通过让他们从事具有挑战性的理论研究和解决复杂的实际问题，为总部的高级培训做出贡献。