Exact and heuristic algorithms for vehicle routing

用于车辆路线的精确启发式算法

• 批准号: RGPIN-2017-05683
• 负责人: Desaulniers, Guy
• 金额: $ 3.72万
• 依托单位: École Polytechnique de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=759408
• 关键词: Exact; heuristic; algorithms; vehicle; routing

Vehicle routing problems consist of determining least-cost vehicle routes to perform a set of tasks while respecting operational constraints, such as vehicle capacity and service time windows at the customers. Several problem variants exist, arising from different applications in, e.g., freight transportation and humanitarian logistics. In this proposal, we develop mathematical optimization models and methods, called branch-price-and-cut methods, to help the planners determine the best possible vehicle routes. The optimization methods can be classified into two categories: an exact method seeks an optimal solution and can require substantial computational time; a heuristic method is much faster but provides a good-quality solution that may not be optimal. We explore four research directions for exact methods and one for heuristics. In particular, we aim at integrating machine learning algorithms developed in artificial intelligence into branch-price-and-cut algorithms that are widely used for vehicle routing. Furthermore, we develop optimization methods for two specific vehicle routing problems: 1) the simultaneous pickup and delivery problem with time windows and a load fragility constraint that forbids stacking heavy items over light ones and 2) the vehicle routing problem with a moving depot that arises in humanitarian logistics when drones are dispatched from a moving helicopter to assess the extent of damage in different zones of a devastated area. From a scientific point of view, the results of this research are methodological and will be applicable to a wide variety of vehicle routing problems in different modes of transportation and even to certain problems arising in other domains. From a commercial point of view, these results will help the freight companies and the humanitarian logistic providers to better manage the utilization of their vehicles and will therefore contribute to increase the productivity of Canadian companies and reduce greenhouse gas emissions. Finally, from an academic point of view, this research will provide financial support and training to three PhD, two MSc and one undergraduate students that will be in demand by the Canadian optimization software development companies after completing their studies.

车辆路线问题包括确定最小成本的车辆路线以执行一组任务，同时尊重操作限制，例如在客户的车辆容量和服务时间窗口。存在几种问题变体，这是由于货运运输和人道主义物流中的不同应用而引起的。在此提案中，我们开发了数学优化模型和方法，称为分支定价方法，以帮助计划者确定最佳的车辆路线。优化方法可以分为两类：一种确切的方法寻求最佳解决方案，并且可能需要大量的计算时间；一种启发式方法要快得多，但提供了可能不是最佳的优质解决方案。我们探索了四个研究方向，以进行精确方法和一个启发式方法。特别是，我们旨在将在人工智能中开发的机器学习算法集成到广泛用于车辆路由的分支机价和切割算法中。此外，我们为两个特定的车辆路线问题开发了优化方法：1）与时间窗口的同时拾取和交付问题以及负载易碎性约束，禁止将重物堆叠在轻度上，以及2）在驱动式载螺旋中，在驱动式载架的范围内，在人道主义物流中派遣了人类物流中出现的车辆路由问题，以评估不同的Zones dep devest of Zones of Ally Zones a devest of a Devest of Ally Zones。从科学的角度来看，这项研究的结果是方法论上的，并且适用于不同运输方式的各种车辆路由问题，甚至适用于其他领域中产生的某些问题。从商业角度来看，这些结果将帮助货运公司和人道主义物流提供者更好地管理其车辆的利用，因此将有助于提高加拿大公司的生产率并减少温室气体的排放。最后，从学术的角度来看，这项研究将为三位博士学位，两名MSC和一名本科生提供财政支持和培训，这些博士学位将在完成研究后得到加拿大优化软件开发公司的要求。