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，它们通常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它们时需要将其考虑在内。原因在于随机环境下的最佳解决方案通常在结构上不同于它们的确定性对应物。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（即在不同的上下文场景下保持高效）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。现在更容易获得通信和信息技术，并以各种方式加以应用，以获得有关问题参数的更多信息，并更好地了解不确定性是如何出现和影响这些问题的。然而，需要自适应的随机优化模型来适当地制定涉及不确定性问题的决策动力学，并有效地利用与上下文有关的可用信息。目前的研究计划致力于在解决交通（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。在本计划中定义了三个一般的研究领域。前两部分重点讨论了所考虑的问题：1区动态随机车辆路径模型和2区随机网络设计模型。在这些领域，将进行一般项目：在区域1，随机车辆路线问题的一致性（项目1.1）和动态城市车辆路线问题（项目1.2）；在区域2中，设计稳健和弹性网络（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，它致力于开发随机问题的一般解决策略（区域3方法论开发），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).