Stochastic optimization methods for vehicle routing and network design problems

每当组合优化问题出现在实际环境中时，往往涉及到一定程度的不确定性(随机环境)，在开发适当的模型来解决它们时，需要考虑这种不确定性。这样做的原因是，在随机环境中最优的解决方案通常在结构上不同于它们的确定性对应的解决方案。在不确定的环境中，当公司或组织面临这样的问题时，他们需要找到既要健壮(即在不同的情景下保持高效)又要有弹性(即能够在意外事件发生时快速反弹)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，以获得关于问题参数的更多信息，并更好地了解不确定性是如何出现和影响这些问题的。然而，需要适当的随机优化模型来适当地表达涉及不确定性的问题的决策动态，并有效地利用与背景有关的可用信息。目前的研究计划致力于在解决运输(车辆路径)和物流(网络设计)问题的背景下研究这一重要问题。本方案界定了研究的三个一般领域。前两个问题集中在所考虑的问题上：区域1的动态随机车辆路径模型和区域2的随机网络设计模型。在这些领域，将开展一般性项目：在领域1，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)；在区域2，设计稳健和有弹性的网络(项目2.1)和不确定情况下的能力规划(项目2.2)。至于第三个领域，它致力于制定随机问题的一般解决战略(领域3方法论发展)，这将在三个项目中完成：分解战略(项目3.1)、混合解决方法(项目3.2)和并行方法(项目3.3)。

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).