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Uncertain multi-objective optimization for the water-rail-road intermodal transport system with consideration of hub operation process using a memetic algorithm

机译:利用MECET算法考虑集线器操作过程的防水道通道传输系统不确定的多目标优化

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摘要

This paper addresses the multi-objective optimization of water-rail-road (WRR) intermodal transport system under uncertainty by explicitly capturing intermodal hub operation activities. Through the use of hub-and-spoke-type network, we formulate an uncertain multi-objective programming model for the WRR intermodal transportation network design problem, in which the cost, time and reliability objectives are simultaneously considered. Subsequently, we turn the original model into a deterministic equivalent multi-objective programming model under mild assumptions. Eventually, we utilize the epsilon-constraint method to reformulate the crisp multi-objective programming model to a modified mono objective one, which has proven to be NP-hard. Hence, we develop a memetic algorithm (MA) by combining a genetic algorithm and local intensification to solve the proposed problem. When designing the MA, we propose a combination encoding scheme to represent the location of intermodal hubs, the allocation of the demand nodes and the assignment of transportation modes. Moreover, we provide two local intensification operators to enhance exploitation ability. Finally, we implement a series of numerical experiments based on the Turkish network data set to verify the practicability of the proposed model and effectiveness of the solution approach developed in the paper.
机译:本文通过明确捕获多式联运集线器操作活动,解决了不确定性下的水铁路(WRR)多式联运系统的多目标优化。通过使用轮毂和辐条型网络,我们为WRR多式联运网络设计问题制定了一个不确定的多目标编程模型,其中同时考虑了成本,时间和可靠性目标。随后,我们将原模在温和的假设下将原始模型转变为确定性等同的多目标编程模型。最终,我们利用epsilon-crountraint方法来重构清脆的多目标编程模型到改进的单声道目标,已被证明是np-hard。因此,通过组合遗传算法和局部强化来解决遗传算法和局部强化来开发迭代算法(MA)。在设计MA时,我们提出了一种组合编码方案来表示多式联运集线器的位置,需求节点的分配和运输模式的分配。此外,我们提供了两个本地强化运营商,以提高利用能力。最后,我们基于土耳其网络数据集实现了一系列数值实验,以验证纸张中开发的解决方案方法的建议模型和有效性的实用性。

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