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Particle swarm optimization for uncapacitated multiple allocation hub location problem under congestion

机译：拥塞情况下无容量多分配枢纽选址问题的粒子群优化

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摘要

In this paper we address the hub location problem in which capacity restrictions are introduced into the objective function as a penalty cost to represent their congestion effects on respective hubs. The goal of hub location problems is to locate an optimal set of hubs while minimizing sum of the transportation and opening costs under various constraints such as number of hubs, delay time and capacities of hubs. Moreover, we propose a more realistic variant of the problem where the capacities of hubs are not only a limitation in selecting the hubs. In practice, capacities of hubs are predicted by a strategic plan, but these predictions generally fail to meet the expectations due to changes in the flow in future and competitive nature of air transportation. A particle swarm optimization (PSO) is proposed to handle the complex nature of this problem and shorten the computing times. The performance of the proposed PSO is analyzed comparatively on the Australia Post (AP) data sets. The numerical results show that the proposed method can find the optimal solutions in less computation time in comparison with the earlier multiple allocation hub location models.
机译：在本文中，我们解决了集线器位置问题，在该问题中，将容量限制引入目标函数作为惩罚成本，以表示它们对各个集线器的拥塞影响。轮毂位置问题的目的是找到一套最佳的轮毂，同时在各种限制（例如轮毂数量，延迟时间和轮毂容量）下使运输和开放成本的总和最小化。此外，我们提出了一个更现实的问题变体，其中集线器的容量不仅是选择集线器的限制。实际上，枢纽的容量是由战略计划预测的，但是由于未来流量的变化和航空运输的竞争性质，这些预测通常无法达到预期。提出了一种粒子群算法（PSO）来解决该问题的复杂性并缩短计算时间。拟议的PSO的性能在澳大利亚邮政（AP）数据集上进行了比较分析。数值结果表明，与较早的多分配枢纽定位模型相比，该方法能在较少的计算时间内找到最优解。

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