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A two-stage heuristic method for vehicle routing problem with split deliveries and pickups

机译:具有分批交货和取货的车辆路径问题的两阶段启发式方法

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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.
机译:车辆路径问题(VRP)是运输和物流网络系统中众所周知的组合优化问题。传统VRP存在一些限制。在过去的几十年中,解除传统VRP的限制条件已成为研究的重点。特别提出了运输和提货分开的车辆路径问题(VRPSPDP),以解除对每个客户的访问时间和车辆容量的限制,也就是说,允许每个客户同时进行多次提货和运输。很少有研究关注VRPSPDP问题。本文提出了一种将初始启发式算法和混合启发式算法相结合的两阶段启发式方法来研究VRPSPDP问题。为了验证所提出的算法,修改了所罗门基准数据集和扩展的所罗门基准数据集,以与其他三种流行算法进行比较。总共18个数据集用于评估该方法的有效性。计算结果表明,所提出的算法在总出行成本和平均装载率方面优于VRPSPDP的这三种算法。

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