首页> 外文期刊>Computational intelligence and neuroscience >Optimizing the Shunting Schedule of Electric Multiple Units Depot Using an Enhanced Particle Swarm Optimization Algorithm
【24h】

Optimizing the Shunting Schedule of Electric Multiple Units Depot Using an Enhanced Particle Swarm Optimization Algorithm

机译:利用增强粒子群优化算法优化电气多单位仓库的分流时间表

获取原文
获取原文并翻译 | 示例
           

摘要

The shunting schedule of electric multiple units depot (SSED) is one of the essential plans for high-speed train maintenance activities. This paper presents a 0-1 programming model to address the problem of determining an optimal SSED through automatic computing. The objective of the model is to minimize the number of shunting movements and the constraints include track occupation conflicts, shunting routes conflicts, time durations of maintenance processes, and shunting running time. An enhanced particle swarm optimization (EPSO) algorithm is proposed to solve the optimization problem. Finally, an empirical study from Shanghai South EMU Depot is carried out to illustrate the model and EPSO algorithm. The optimization results indicate that the proposed method is valid for the SSED problem and that the EPSO algorithm outperforms the traditional PSO algorithm on the aspect of optimality.
机译:电气多单位仓库(SSED)的分流时间表是高速列车维护活动的基本计划之一。 本文介绍了0-1编程模型,以解决通过自动计算确定最佳SSED的问题。 该模型的目的是最大限度地减少分流运动的次数,并且约束包括跟踪占用冲突,分流路线冲突,维护过程的时间持续时间,以及分流运行时间。 提出了一种增强的粒子群优化(EPSO)算法来解决优化问题。 最后,执行了上海南欧仓库的实证研究,以说明模型和EPSO算法。 优化结果表明,所提出的方法对SSED问题有效,并且EPSO算法优于最优性方面的传统PSO算法。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号