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Beam search heuristics for the single and multi-commodity capacitated Multi-facility Weber Problems

机译:针对单商品和多商品的多设施韦伯问题的波束搜索启发法

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摘要

The Multi-facility Weber Problem (MWP) is concerned with locating / uncapacitated facilities in the plane to satisfy the demand of J customers with the minimum total transportation cost of a single commodity. It is a non-convex optimization problem and difficult to solve. In this work, we focus on the capacitated extensions of the MWP which are Capacitated MWP (CMWP) and multi-commodity CMWP (MCMWP). Both the CMWP and MCMWP impose capacity restrictions on facilities. Indeed, the MCMWP is a natural extension of the CMWP and considers the situation where K distinct commodities are shipped subject to limitations on the total amount of goods sent from facilities to the customers. Customer locations, demands and capacities for each commodity are known a priori. The transportation costs, which depend on the commodity type, are proportional to the distance between customers and facilities. We first introduce branch and bound algorithms for both the CMWP and the MCMWP then we propose beam search heuristics for these problems. According to our computational experiments on standard and randomly generated test instances, we can say that the new heuristics perform very well.
机译:多设施韦伯问题(MWP)与飞机中的定位/无能力设施有关,以最小的单个商品的总运输成本满足J客户的需求。这是一个非凸优化问题,难以解决。在这项工作中,我们将重点放在MWP的功能扩展上,即MWP(CMWP)和多商品CMWP(MCMWP)。 CMWP和MCMWP都对设施施加了容量限制。的确,MCMWP是CMWP的自然延伸,它考虑了从工厂向客户发送的商品总量受到限制的情况下装运K种不同商品的情况。先验地知道每种商品的客户位置,需求和能力。运输成本取决于商品类型,与客户和设施之间的距离成正比。我们首先介绍针对CMWP和MCMWP的分支定界算法,然后针对这些问题提出波束搜索启发式算法。根据我们在标准和随机生成的测试实例上的计算实验,我们可以说新的启发式方法表现非常好。

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