工业工程 ›› 2020, Vol. 23 ›› Issue (3): 132-137.doi: 10.3969/j.issn.1007-7375.2020.03.017

• 实践与应用 • 上一篇    下一篇

基于损坏车辆分布预测与损益阈值分析下的共享单车回收研究

张巍, 李鹏翔, 仝自强, 岳毅蒙   

  1. 西安交通大学 管理学院,陕西 西安 710049
  • 收稿日期:2019-03-30 发布日期:2020-07-04
  • 作者简介:张巍(1996-),男,河南省人,硕士研究生,主要研究方向为物流优化
  • 基金资助:
    国家社会科学基金资助项目(14XGL002)

Research on Shared Bicycle Recycling Based on Distribution Prediction of Broken Vehicles and Profit and Loss Threshold Analysis

ZHANG Wei, LI Pengxiang, TONG Ziqiang, YUE Yimeng   

  1. School of Management, Xi'an Jiao Tong University, Xi'an 710049, China
  • Received:2019-03-30 Published:2020-07-04

摘要: 针对共享单车系统在运营过程中损坏车辆造成运营成本及损失愈多的情况,对损坏车辆在站点网络的分布作出预测,基于回收成本阈值分析建立损坏车辆回收网络模型,在此基础上构建以回收成本最小化为目标的回收路由指派模型,并使用禁忌搜索法与遗传算法的混合算法求解实际案例。结果表明:该方法可解决现实情境下,共享单车损坏车辆回收过程的分布预测和路径优化问题。本文的实例分析表明,建立的模型与传统回收方法相比,成本节约了8.72%,不仅降低回收过程的作业成本,同时也可以提高回收效率,对于共享单车运营商的决策优化具有一定参考价值。

关键词: 共享单车, 损坏车辆回收网络, 回收路由, 遗传算法, 禁忌搜索法

Abstract: Seeing the fact that the losses and costs caused by bad bicycles continue to grow in the operation of Shared Bicycle System and forecasting the distribution of bad bicycles in the site network, a damaged bicycle recycling network model is established based on cost threshold analysis, a recycling route assignment model is built with the goal of minimizing recycling costs and the actual case solved as well using the hybrid algorithm of tabu search and genetic algorithm. The results show that the method can solve the problem of distribution prediction and path optimization in the recycling process of damaged bicycles in real-situations. In the example analysis, compared with the traditional recycling method, the established model has a cost saving of 8.72% and an excellent application value, which not only reduces the operating cost of the recycling process, but also improves the recycling efficiency, which has certain reference value for decision-making optimization of shared bicycle operators.

Key words: bike sharing, damaged bicycle recycling network, recycle routes, genetic algorithm, tabu search algorithm

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