Designing Public Service System with Random Demands Using Truncated Path-Relinking Method

J. Janáček, Marek Kvet
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Abstract

Heuristic optimization methods can support the decision-making process in many areas of human activities. Contrary to the exact methods based on mathematical programming principles, the heuristic algorithms are able to produce a near-to-optimal solution in acceptable computational time. However, the accuracy of the produced solution is usually paid for by using the specifics of the solved problem for the development of the algorithm and the careful implementation of related procedures. This paper is focused on a broad class of location problems, which distinguish from other ones by a fixed number of facilities to be located. The studied objective is to deploy the given number of facilities in a given set of possible facility locations so that average user’s disutility is minimal. Within the paper, a special heuristics equipped with the shrinking fence strategy is studied. The strategy consists in the idea that a starting uniformly deployed set of the problem solutions are considered to be a set of stakes of a hypothetical fence, which rounds a flock of animals corresponding to good solutions of the p-location problem. The algorithm inspects the links connecting neighboring stakes and determines set of new stake positions depending on the inspection result. Before a new fence is constituted, the set of new stakes is reduced by elimination of the stakes, positions of which are distant less than a given distance limit. The process of new fence forming is repeated until a termination condition is met and the best found solution obtained by link inspections is returned as the resulting solution. To inspect a fence link, path-relinking method is used. This paper is focused on acceleration of the algorithm by truncation of the inspected link and application of an adaptive termination rule. To find impact of the adjustments on result quality and computational time, a computational study with real-sized benchmarks was performed and associated findings are summarized.
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用截断路径重链接法设计随机需求公共服务系统
启发式优化方法可以支持人类活动许多领域的决策过程。与基于数学规划原理的精确方法相反,启发式算法能够在可接受的计算时间内产生接近最优的解决方案。然而,所产生的解决方案的准确性通常是通过使用所解决问题的细节来支付的,用于算法的开发和相关程序的仔细实施。本文的重点是一类广义的定位问题,它与其他问题的区别在于要定位的设施数量是固定的。所研究的目标是在给定的一组可能的设施位置中部署给定数量的设施,以使平均用户的负效用最小。本文研究了一种特殊的基于缩小围栏策略的启发式算法。该策略包含这样的思想,即一组统一部署的问题解决方案被认为是假想围栏的一组木桩,这些木桩围绕着一群动物,这些动物对应于p-定位问题的好解。该算法检查连接相邻木桩的链路,并根据检查结果确定一组新的木桩位置。在建立新栅栏之前,通过消除距离小于给定距离限制的桩来减少新桩的集合。重复新栅栏形成的过程,直到满足终止条件,并返回由链路检查获得的最佳解作为最终解。为了检查栅栏链路,使用路径重链接方法。本文重点研究了通过截断被检测链路和应用自适应终止规则来提高算法的速度。为了找出调整对结果质量和计算时间的影响,进行了一项具有实际尺寸基准的计算研究,并总结了相关发现。
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