Stochastic optimization of network design and transportation problems

网络设计和运输问题的随机优化

• 批准号: RGPIN-2018-05390
• 负责人: Rei, Walter
• 金额: $ 2.26万
• 依托单位: Université du Québec à Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=684081
• 关键词: Stochastic; optimization; network; design; transportation

Transportation and logistics systems play a paramount role in how modern economies and societies function. The ever-increasing volumes of people and freight that need to be efficiently transported worldwide to properly support economic and social activities while, at the same time, minimize the negative footprint caused by these movements, pose complex planning problems for the decision makers managing these systems. The complexity of such problems is considerably increased by the presence of various sources of uncertainty that randomly and drastically change the contexts in which such planning is conducted. Therefore, solving transportation and logistics problems while explicitly considering all relevant sources of uncertainty has become an important issue to address by both companies, who are continuously striving to remain competitive on the markets, and governments, who are tasked with reducing the negative effects associated with transportation. In all cases, solving such problems poses significant challenges that the present proposal will meet.******Two general types of optimization problems, which are central to the planning and operation of transportation and logistics systems, will be studied: dynamic and stochastic vehicle routing problems and stochastic network design problems. My short-term objectives are twofold: 1) to create novel stochastic optimization methods, by combining machine learning techniques, parallel computation and mathematical programming strategies, to efficiently solve the studied problems and 2) design analytical tools capable of systematically assessing the overall effects of the various sources of uncertainty on the decision-making processes.******Therefore, the various studies that will be performed in the present proposal will bring significant benefits in the form of new and efficient decision support tools for a series of important optimization problems under uncertainty that arise in the context of planning and operating transportation and logistics systems. From a scientific perspective, this proposal will push boundaries by bridging the fields of machine learning and stochastic optimization. Ultimately, the research program will produce a novel methodological framework to assess and study the impacts of uncertainty on optimization problems and enable such problems to be efficiently solved for realistically sized instances (i.e., such as they appear in practical settings).

运输和物流系统在现代经济和社会的运作方式中起着至关重要的作用。 不断增加的人和货运需要有效地在全球范围内进行有效运输，以适当地支持经济和社会活动，同时，最大程度地减少了由这些运动造成的负面足迹，为管理这些系统的决策者带来了复杂的计划问题。由于存在各种不确定性来源，这些问题的复杂性大大增加了，这些不确定性是随机和大幅度改变进行此类计划的背景的。因此，解决运输和物流问题的同时明确考虑所有相关的不确定性来源已成为要解决两家公司的重要问题，这些公司一直在努力保持在市场上保持竞争力，以及负责减少与负面影响相关的负面影响的政府运输。 在所有情况下，解决此类问题都会构成本提案将遇到的重大挑战。随机车辆路由问题和随机网络设计问题。我的短期目标是双重的：1）通过结合机器学习技术，并行计算和数学编程策略来创建新颖的随机优化方法，以有效地解决研究的问题； 2）设计分析工具，能够系统地评估整体评估的整体影响**********************************在规划和运营运输和物流系统背景下出现的不确定性下的优化问题。从科学的角度来看，该提案将通过桥接机器学习和随机优化的领域来突破界限。最终，该研究计划将产生一个新颖的方法学框架，以评估和研究不确定性对优化问题的影响，并使这些问题能够有效地解决现实尺寸的实例（即，例如在实际环境中出现）。