Abstract The bike repositioning and recycling problem (BRRP) is significant to develop a sustainable bike-sharing system and can effectively reduce the imbalance between demand and supply. This study investigates the static repositioning and recycling problem of a bike-sharing system, which is formulated as an integer linear programming model. The BRRP is a variant of the multi-depot simultaneous pickup and delivery problem with multi-commodity demand. To solve the proposed model, an adaptive tabu search (ATS) algorithm combined with six neighborhood structures is developed. Moreover, an iterated local search (ILS) and a route elimination operator are both embedded to reduce the number of routes. The performance of the proposed ATS is evaluated by comparing it with tabu search (TS) and variable neighborhood search (VNS). The experimental results show that the proposed algorithm performs better than TS and VNS in terms of the solution quality. The proposed ATS was used to analyze the bicycle system in New York City. Finally, a free solver is developed for the bike repositioning and recycling problem, which is named the BRRP Solver.

中文翻译：

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一种嵌入迭代局部搜索和路线消除的自适应禁忌搜索算法解决自行车重新定位和回收问题

摘要 自行车重新定位和回收问题（BRRP）对于发展可持续的共享单车系统具有重要意义，可以有效减少供需失衡。本研究调查了共享单车系统的静态重新定位和回收问题，该系统被表述为整数线性规划模型。BRRP 是具有多种商品需求的多仓库同时提货和交货问题的变体。为了解决所提出的模型，开发了一种结合六个邻域结构的自适应禁忌搜索 (ATS) 算法。此外，还嵌入了迭代本地搜索 (ILS) 和路线消除算子以减少路线数量。通过将其与禁忌搜索 (TS) 和可变邻域搜索 (VNS) 进行比较来评估所提出的 ATS 的性能。实验结果表明，所提出的算法在求解质量方面优于TS和VNS。提议的 ATS 用于分析纽约市的自行车系统。最后，针对自行车重新定位和回收问题开发了一个免费求解器，称为 BRRP 求解器。