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Heuristics Based on Genetic Algorithms for the Capacitated Multi Vehicle Production Distribution Problem

机译:基于遗传算法的启发式多车辆产量分配问题

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In this paper, we consider the integration of production, inventory and distribution decisions in a supply chain composed of one production facility supplying several retailers located in the same region. The supplier is far from the retailers compared to the distance between retailers. Thus, the traveling cost of each vehicle from the supplier to the region is assumed to be fixed and there is a fixed delivery (service) cost for each visited retailer. The objective is to minimize the sum of the costs at the production facility and at the retailers. The problem is more general than the One-Warehouse Multi-Retailer problem and is a special case of the Production Routing Problem. Five heuristics based on a Genetic Algorithm are proposed to solve the problem. In particular, three of them include the resolution of a Mixed Integer Program as subproblem to generate new individuals in the population. The results show that the heuristics can find optimal solutions for small and medium size instances. On large instances, the gaps obtained by the heuristics in less than 300 s are better than the ones obtained by a standard solver in two hours. (C) 2018 Elsevier Ltd. All rights reserved.
机译:在本文中,我们考虑将生产,库存和分销决策整合到一个供应链中,该供应链由一个生产设施提供给位于同一地区的多家零售商。与零售商之间的距离相比,供应商离零售商很远。因此,假设每辆车从供应商到区域的行驶成本是固定的,并且每个拜访的零售商都有固定的交付(服务)成本。目的是使生产设施和零售商的成本总和最小化。该问题比单仓库多零售商问题更普遍,并且是生产工艺路线问题的特例。提出了五种基于遗传算法的启发式算法。特别是,其中三个包括将混合整数程序作为子问题来解决,以在人口中产生新的个体。结果表明,启发式算法可以为中小型实例找到最佳解决方案。在大型情况下,启发式方法在不到300 s内获得的间隙要比标准求解器在两小时内获得的间隙要好。 (C)2018 Elsevier Ltd.保留所有权利。

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