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Journal of Zhejiang University SCIENCE C
ISSN 1869-1951(Print), 1869-196x(Online), Monthly
2014 Vol.15 No.3 P.200-210
A two-stage heuristic method for vehicle routing problem with split deliveries and pickups
Abstract: The vehicle routing problem (VRP) is a well-known combinatorial optimization issue in transportation and logistics network systems. There exist several limitations associated with the traditional VRP. Releasing the restricted conditions of traditional VRP has become a research focus in the past few decades. The vehicle routing problem with split deliveries and pickups (VRPSPDP) is particularly proposed to release the constraints on the visiting times per customer and vehicle capacity, that is, to allow the deliveries and pickups for each customer to be simultaneously split more than once. Few studies have focused on the VRPSPDP problem. In this paper we propose a two-stage heuristic method integrating the initial heuristic algorithm and hybrid heuristic algorithm to study the VRPSPDP problem. To validate the proposed algorithm, Solomon benchmark datasets and extended Solomon benchmark datasets were modified to compare with three other popular algorithms. A total of 18 datasets were used to evaluate the effectiveness of the proposed method. The computational results indicated that the proposed algorithm is superior to these three algorithms for VRPSPDP in terms of total travel cost and average loading rate.
Key words: Vehicle routing problem with split deliveries and pickups (VRPSPDP), Two-stage heuristic method, Hybrid heuristic algorithm, Solomon benchmark datasets
创新要点:提出两阶段启发式方法,即可行解获取的初始启发式算法和改进初始解的混合启发式算法,为研究车辆路径问题需求拆分和访问次数的松弛提供了有效的求解思路。引用描述该问题实质的逻辑关系模型,结合定义和化简的概念,本文算法为集送货可拆分车辆路径问题的研究,提供了分步计算的实用算法。
方法提亮:组成两阶段启发式方法的各种逻辑关系模型简洁、准确地描述了集送货可拆分车辆路径问题,同时具备可扩展性和可组合性。这些特性的灵活应用,形成本文提出的两阶段优化算法。
重要结论:利用Solomon数据集和可扩展的Solomon数据集进行实验计算,并与禁忌搜索算法(TSA),粒子群算法(PSO)以及并行启发式方法(PHA)比较,结果表明,对于不同规模数据集,本文算法有效节省了总配送成本、提升了平均装载率。
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DOI:
10.1631/jzus.C1300177
CLC number:
TP3; TU121
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2024-08-27
Received:
2023-10-17
Revision Accepted:
2024-05-08
Crosschecked:
2014-02-19