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A Hybrid Metaheuristic Algorithm For The Vehicle Routing Problem With Simultaneous Delivery And Pick-up Service

机译：具有同时交付和接送服务的车辆路径问题的混合元启发式算法

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摘要

This article addresses a vehicle routing problem variant which considers customers to require simultaneous delivery and pick-up service (VRPSPD). The objective of this problem is to determine the optimal set of routes to totally satisfy both the delivery and pick-up demand of the customer population. VRPSPD is an NP-hard combinatorial optimization problem; therefore exact methods are incapable of dealing with large scale VRPSPD instances arising in a wide variety of practical operations. We propose a hybrid solution approach incorporating the rationalc of two well-known metaheuristics which have proven to be effective for routing problem variants, namely tabu search and guided local search. The intelligence of the proposed hybrid was designed to achieve a vast exploration of the search space, by escaping from local optima and intensifying at promising solution regions. The performance of our metaheuristic algorithm was tested on benchmark instances involving from 50 to 400 customers. It produced high quality results, improving several best solutions previously reported.
机译：本文介绍了一种车辆路线选择问题变体，该变体考虑到客户需要同时交付和接送服务（VRPSPD）。该问题的目的是确定最佳路线集，以完全满足客户群体的运送和接送需求。 VRPSPD是一个NP难的组合优化问题。因此，确切的方法无法处理各种实际操作中产生的大规模VRPSPD实例。我们提出了一种混合解决方案方法，该方法结合了两种众所周知的元启发式算法的原理，这两种方法已被证明可有效地解决问题变体，即禁忌搜索和引导式局部搜索。通过避开局部最优值并在有希望的解决方案区域进行增强，提出的混合动力的智能性旨在实现对搜索空间的广泛探索。我们的元启发式算法的性能在涉及50到400个客户的基准实例上进行了测试。它产生了高质量的结果，改进了先前报道的几种最佳解决方案。

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