Abstract The Container Drayage Problem (CDP) aims at routing a fleet of trucks, based at a common terminal, to serve customers while minimizing the total travel distance. Each trip starts from and ends at the terminal, and handles a subset of customers. Each customer requires either that a container is picked up or delivered. We introduce a more realistic variant, i.e., the Multi-trip Multi-period CDP with Release and Due Dates (MM-CDP-RDD), in which the planning horizon is composed of several periods (days). On each day, each truck may perform more than one trip respecting the Release and Due Dates (RDD) associated with customer services, corresponding to the first and the last day on which the service can be carried out, respectively. Drivers’ contracts impose limitations on the maximum driving time allowed on each day, on two consecutive days and on the whole weekly planning horizon. To model the MM-CDP-RDD, we propose both an Arc-based Integer Linear Programming (ILP) formulation and a Trip-based ILP formulation that receives as input all the feasible non-dominated trips. To efficiently address medium/large-sized instances of the problem, we also design six Combinatorial Benders’ Cuts approaches. All the methods are compared on a rich set of instances generated for this new problem.

中文翻译：

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具有发布和到期日的多期多程集装箱拖运问题

摘要 集装箱拖运问题 (CDP) 旨在为基于公共码头的卡车车队安排路线，为客户提供服务，同时最大限度地减少总行驶距离。每次旅行都从终点站开始并在终点站结束，并处理一部分客户。每个客户都要求提取或交付一个容器。我们引入了一个更现实的变体，即具有发布和截止日期的多行程多周期 CDP (MM-CDP-RDD)，其中计划范围由多个周期（天）组成。在每一天，每辆卡车可以根据与客户服务相关的发布和到期日期 (RDD) 执行多次行程，分别对应于可以执行服务的第一天和最后一天。司机的合同对每天允许的最长驾驶时间施加了限制，在连续两天和整个每周计划范围内。为了对 MM-CDP-RDD 进行建模，我们提出了基于弧的整数线性规划 (ILP) 公式和基于行程的 ILP 公式，该公式接收所有可行的非支配行程作为输入。为了有效地解决该问题的中/大型实例，我们还设计了六种 Combinatorial Benders' Cuts 方法。所有方法都在为这个新问题生成的丰富实例集上进行了比较。