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A multi-objective optimization-simulation approach for real time rescheduling in dense railway systems

机译:密集铁路系统实时重新安排的多目标优化仿真方法

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Rescheduling trains in dense railway systems to cope in real time with limited disturbances is a challenging problem with multiple conflicting objectives and various types of decisions. Based on the French railway system in the Paris region, this paper proposes an approach combining multi-objective optimization, to select rescheduling decisions, and macroscopic simulation, to compute the objectives associated to these decisions. Possible decisions include canceling or short-turning trains and skipping or adding stops. Three main objectives are optimized to propose multiple solutions to the decision makers: The recovery time, the quality of service for passengers and the number of decisions. Two greedy heuristics are presented whose results on actual data are compared with a full enumeration method. The multiobjective feature of the approach is also analyzed. The implementation and successful validation in real life of a decision-support tool, that is now implemented, is discussed. (C) 2020 Elsevier B.V. All rights reserved.
机译:重新安排致密铁路系统的列车以实时应对有限的扰动是一种具有挑战性的问题,具有多种相互矛盾的目标和各种类型的决策。基于法国铁路系统在巴黎地区,本文提出了一种组合多目标优化的方法,选择重新安排决策和宏观模拟,计算与这些决策相关的目标。可能的决定包括取消或短路列车并跳过或添加停止。三个主要目标经过优化,向决策者提出多种解决方案:恢复时间,乘客的服务质量和决策的数量。介绍了两个贪婪的启发式机,其结果与完整的枚举方法进行了比较。还分析了该方法的多目标特征。讨论了现在实施的决策支持工具的现实生活中的实现和成功验证。 (c)2020 Elsevier B.v.保留所有权利。

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