Exact and Heuristic Methods for the Split Delivery Vehicle Routing Problem

车辆路径问题 水准点(测量) 布线(电子设计自动化) 启发式 数学优化 计算机科学 趋同(经济学) 分支和切割 钥匙(锁) 拉格朗日松弛 放松(心理学) 整数规划 计算机网络 数学 经济 计算机安全 经济增长 社会心理学 大地测量学 心理学 地理
作者
Mette Gamst,Richard Martin Lusby,Stefan Røpke
出处
期刊:Transportation Science [Institute for Operations Research and the Management Sciences]
卷期号:58 (4): 741-760 被引量:5
标识
DOI:10.1287/trsc.2022.0353
摘要

This paper describes an exact branch-and-cut (B&C) algorithm for the split delivery vehicle routing problem. The underlying model is based on a previously proposed two-index vehicle flow formulation that models a relaxation of the problem. We dynamically separate two well-known classes of valid inequalities, namely capacity and connectivity cuts, and use an in-out algorithm to improve the convergence of the cutting phase. We generate no-good cuts from feasible integer solutions to the relaxation using a recently proposed single-commodity flow formulation in the literature. The exact methodology is complemented by a very effective adaptive large neighborhood search (ALNS) heuristic that provides high-quality upper bounds to initiate the B&C algorithm. Key ingredients in the design of the heuristic include the use of a tailored construction algorithm, which can exploit the situation in which the ratio of the number of customers to the minimum number of vehicles needed is low, and the use of a route-based formulation to improve the solutions found before, during, and after the ALNS procedure. An earlier version of this work was submitted to the DIMACS (Center for Discrete Mathematics and Theoretical Computer Science) implementation challenge, where it placed third. On sets of well-known benchmark instances for limited and unlimited fleet variants of the problem, we demonstrate that the heuristic provides very competitive solutions, with respective average gaps of 0.19% and 0.18% from best-known values. Furthermore, the exact B&C framework is also highly competitive with state-of-the-art methods, providing solutions with an average optimality gap of 1.82%. History: This paper has been accepted for the Transportation Science Special Section on DIMACS Implementation Challenge: Vehicle Routing Problems. Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2022.0353 .
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