Study on bike repositioning problem with rental and return demand

被引：0
|
作者：
Cui C. [1 ]
Tian Z. [2 ]
Xu Y. [2 ]
机构：
[1] School of Computer and Information Engineering, Henan University of Economics and Law, Zhengzhou
[2] School of Information, Beijing Wuzi University, Beijing
来源：
Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice | 2024年 / 44卷 / 02期
基金：
中国国家自然科学基金;
关键词：
artificial bee colony-greedy algorithm; bike sharing system; linearization; rental and return demand; static repositioning;
D O I：
10.12011/SETP2023-1462
中图分类号：
学科分类号：
摘要：
When users go to the station for bicycle rental or return, if there are no bicycles or parking spaces at the station, it will make the user’s rental or return needs unable to be met, resulting in losses to the company. Facing this challenge, this study develops a nonlinear static repositioning optimization model for BSS, which considers two periods, namely operation and shutdown periods. The bike repositioning occurs during shutdown period. In the model, the objective functions include the repositioning costs and unmet service cost, the decision variables are truck activation, travel routes, and vehicle repositioning between stations, and the state variable includes the number of bikes at each station during the operation period. Analyses are conducted on the dynamic evolution process of the state variable, which is caused by repositioning strategy and the interaction of rental and return demands between stations. The internal logic of the repositioning is also analyzed, and a linearization method is employed to linearize the model. Then, an artificial bee colony-greedy algorithm is designed to solve large-scale problems based on the problem characteristics. Finally, numerical examples are used to analyze the problem properties and algorithm’s performance. The results show that the unit unmet service cost and repositioning capacity have significant impacts on repositioning optimization. The advantages of the artificial bee colony-greedy algorithm in solving large-scale problems have been verified. This research can provide decision support for the repositioning of BSS. © 2024 Systems Engineering Society of China. All rights reserved.
引用
收藏
页码：645 / 660
页数：15
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