Stochastic optimization methods for vehicle routing and network design problems

车辆路径和网络设计问题的随机优化方法

• 批准号: 355401-2013
• 负责人: Rei, Walter
• 金额: $ 1.53万
• 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: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=636727
• 关键词: Stochastic; optimization; methods; vehicle; routing

Whenever they appear in practical settings, combinatorial optimization problems oftentimes involve a level of uncertainty (stochastic environment) that needs to be factored in when developing appropriate models to solve them. The reasons for this being that solutions that are optimal in stochastic settings are usually structurally different from their deterministic counterparts. In an uncertain environment, when companies or organizations face such problems, they need to find solutions that need to be both robust (i.e., remain efficient under different contextual scenarios) and resilient (i.e., able to quickly rebound when unexpected events occur). Communicational and informational technologies are now more readily available and applied in various ways to obtain more information on the parameters of the problems and get a better understanding of how uncertainty appears and affects them. However, adapted stochastic optimization models are needed to properly formulate the decisional dynamics of problems that involve uncertainty and to efficiently utilize the available information pertaining to the context. The present research program is dedicated to the study of this important issue in the context of solving both transportation (vehicle routing) and logistics (network design) problems. Three general areas of research are defined in the present program. The first two focus on the problems considered: Area 1 Dynamic and stochastic vehicle routing models and Area 2 Stochastic network design models. In these areas, general projects will be undertaken: in Area 1, Consistency in stochastic vehicle routing problems (project 1.1) and Dynamic urban vehicle routing problems (project 1.2); in Area 2, Designing robust and resilient networks (project 2.1) and Capacity planning under uncertainty (project 2.2). As for the third area, it is dedicated to the development of general solution strategies for stochastic problems (Area 3 Methodological development), which will be done in three projects: Decomposition strategies (project 3.1), Hybrid solution methods (project 3.2) and Parallel methods (project 3.3).

每当它们出现在实际环境中时，组合优化问题通常会涉及一定程度的不确定性（随机环境），在开发适当的模型来解决这些问题时需要将其考虑在内。其原因是随机设置中的最佳解决方案在结构上通常与其确定性对应方案不同。在不确定的环境中，当公司或组织面临此类问题时，他们需要找到既稳健（即在不同情境下保持高效）又具有弹性（即在意外事件发生时能够快速反弹）的解决方案。通信和信息技术现在更容易获得并以各种方式应用，以获得有关问题参数的更多信息，并更好地了解不确定性如何出现和影响它们。然而，需要采用适应的随机优化模型来正确地制定涉及不确定性的问题的决策动态，并有效地利用与上下文相关的可用信息。目前的研究计划致力于在解决运输（车辆路线）和物流（网络设计）问题的背景下研究这一重要问题。本计划定义了三个一般研究领域。前两个重点关注所考虑的问题：区域 1 动态和随机车辆路径模型和区域 2 随机网络设计模型。在这些领域中，将开展一般性项目：在区域 1 中，随机车辆路径问题的一致性（项目 1.1）和动态城市车辆路径问题（项目 1.2）；在领域 2，设计强大且有弹性的网络（项目 2.1）和不确定性下的容量规划（项目 2.2）。至于第三个领域，致力于开发随机问题的通用解决策略（领域3方法开发），这将在三个项目中完成：分解策略（项目3.1）、混合解决方法（项目3.2）和并行解决方案。方法（项目 3.3）。