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Integrated production and multiple trips vehicle routing with time windows and uncertain travel times

机译:具有时间窗口和不确定的行驶时间的集成生产和多程车辆路线

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We study the integrated production and multiple trips vehicle routing problem with time windows and uncertain travel times involving two phases. The first phase considers scheduling a set of jobs on parallel machines with machine-dependent ready times, and the second phase focuses on the delivery of completed jobs by a fleet of identical vehicles, which may differ in their ready times. In addition, the travel times in distribution are uncertain. The objective is to minimize the total cost comprising the travel cost and penalty cost caused by tardiness. We present a robustness approach, known as “Elasticp-Robustness”, to deal with travel time variations when historical risk data are limited or non-existent, and develop a memetic algorithm with an effective search strategy to solve the problem. We conduct numerical studies on randomly generated data based on real experience to assess the effectiveness and efficiency of the proposed method. The computational results show that the proposed solution approach yields relatively good solutions in comparison with current mainstream heuristic algorithms.
机译:我们研究了具有两个时段的时间窗和不确定的行驶时间的综合生产和多程车辆路径问题。第一阶段考虑在并行机器上按机器的准备时间安排一组作业,第二阶段着重于由一组相同车辆的完成工作的交付,这些车辆的准备时间可能不同。另外,分配中的旅行时间不确定。目的是最小化由迟到引起的包括差旅费和罚款费在内的总费用。我们提出了一种鲁棒性方法,称为“弹性鲁棒性”,用于处理历史风险数据有限或不存在时的旅行时间变化,并开发一种具有有效搜索策略的模因算法来解决该问题。我们根据实际经验对随机生成的数据进行数值研究,以评估该方法的有效性和效率。计算结果表明,与当前主流的启发式算法相比,所提出的解决方案产生了相对较好的解决方案。

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