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Approximating solutions to a bilevel capacitated facility location problem with customer's patronization toward a list of preferences

机译:近似解压缩电容设施位置问题与客户的光顾朝向偏好列表

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This paper presents a bilevel capacitated facility location problem where customers are allocated to the facilities they patronize based on a predetermined list of preferences. The bilevel problem is composed of an upper level, where a company locates facilities to minimize locating and distributing costs; and a lower level, where customers aim to maximize their preferences by being allocated to the most preferred facilities to get their demands met. The complexity of the lower level problem, which is NP-hard, demands alternatives for obtaining, in general, the follower's rational reaction set. Hence, bilevel attainable solutions are defined for solving the bilevel problem in an efficient manner. Moreover, for obtaining valid bounds, a reformulation of the bilevel problem based on the lower level's linear relaxation is performed. Then, a cross entropy method is implemented for obtaining solutions in the upper level; while the lower level is solved in three different manners: by a greedy randomized adaptive procedure based on preferences, by the same procedure but based on a regret cost, and by an exact method (when possible). The conducted experimentation shows the competitiveness of the proposed algorithms, in terms of solution quality and consumed time, despite the complexity of the problem's components. (C) 2017 Elsevier Inc. All rights reserved.
机译:本文介绍了一台百智能电容设施位置问题,客户被分配给他们根据预定的偏好列表光顾的设施。贝塞尔问题由上层组成,公司定位设施以最大限度地减少定位和分配成本;和较低的级别,客户旨在通过分配给最优先设施来最大限度地提高他们的偏好来实现他们的需求。较低级别问题的复杂性是NP-HARD,需要替代偏移,一般而言,追随者的合理反应集。因此,定义了Bilevel可达到的解决方案以以有效的方式解决彼得问题。此外,为了获得有效界限,执行基于较低级别的线性松弛的彼得问题的重构。然后,实现跨熵方法以获得上层的解决方案;虽然较低级别以三种不同的方式解决:通过相同的过程,基于偏好的贪婪随机自适应过程,但基于遗憾的成本,并通过精确的方法(尽可能何时)。由于问题的组件的复杂性,所进行的实验表明了所提出的算法的竞争力,就解决方案质量和消耗时间。 (c)2017年Elsevier Inc.保留所有权利。

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