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Bi-objective optimization of multi-server intermodal hub-location-allocation problem in congested systems: modeling and solution

机译:拥挤系统中多服务器联运枢纽-位置-分配问题的双目标优化:建模与求解

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A new multi-objective intermodal hub-location-allocation problem is modeled in this paper in which both the origin and the destination hub facilities are modeled as an M/M/m queuing system. The problem is being formulated as a constrained bi-objective optimization model to minimize the total costs as well as minimizing the total system time. A small-size problem is solved on the GAMS software to validate the accuracy of the proposed model. As the problem becomes strictly NP-hard, an MOIWO algorithm with an efficient chromosome structure and a fuzzy dominance method is proposed to solve large-scale problems. Since there is no benchmark available in the literature, an NSGA-II and an NRGA are developed to validate the results obtained. The parameters of all algorithms are tuned using the Taguchi method and their performances are statistically compared in terms of some multi-objective metrics. Finally, the entropy-TOPSIS method is applied to show that MOIWO is the best in terms of simultaneous use of all the metrics.
机译:本文对一个新的多目标联运枢纽-位置-分配问题进行了建模,其中始发和目的地枢纽设施都被建模为M / M / m排队系统。该问题正被公式化为受约束的双目标优化模型,以最大程度地降低总成本并缩短总系统时间。在GAMS软件上解决了一个小型问题,以验证所提出模型的准确性。针对严格的NP问题,提出了一种具有有效染色体结构和模糊优势方法的MOIWO算法来解决大规模问题。由于文献中没有基准可用,因此开发了NSGA-II和NRGA来验证获得的结果。使用Taguchi方法调整所有算法的参数,并根据一些多目标指标对它们的性能进行统计比较。最后,应用熵-TOPSIS方法显示MOIWO在同时使用所有度量方面是最好的。

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