Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 160, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2020.113752
Keywords
Bike-sharing systems; Rebalancing; Depot inventory; Variable neighborhood search
Categories
Funding
- Funds for the Basic and Applied Basic Research Foundation of Guangdong Province of China [2019A1515110399]
- Fundamental Research Funds for the Central Universities [21620360]
- National Natural Science Foundation of China [51875251]
- Key Research and Development Program of Guangdong Province [2019B090921001]
- Guangdong Special Support Talent Program - Innovation and Entrepreneurship Leading Team [2019BT02S593]
- 2018 Guangzhou Leading Innovation Team Program [201909010006]
- Key Project of the Major Research Plan of the National Natural Science Foundation of China [91746210]
- Beijing Municipal Natural Science Foundation [9172016]
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Smart shared mobility is an emerging transportation strategy that promotes sustainable and intelligent transportation. As one mode of smart shared mobility bike sharing is gaining popularity in recent years. A daily rebalancing operation is commonly carried out to keep high level service of bike-sharing systems (BSSs). The static bike-sharing rebalancing problems (SBRPs) studied in existing papers focus on determining the vehicle routes with minimal traveling cost. However, the depot inventory is rarely considered during the relocation. Thus, this paper researches the integration of the depot inventory and vehicle routing problems, with the aim of minimizing the daily operational cost including the depot inventory cost (DIC) and the traveling cost. First, two mixed integer programming (MIP) formulations are proposed to find the daily optimal decision on the vehicle routes and the numbers of bikes and vehicles employed from the depot. Based on the models, an improved general variable neighborhood search (IGVNS) algorithm is developed with a variety of neighborhood structures and a hybrid strategy. Finally, we apply a set of benchmark instances to test our proposed model and approach, and the computational results demonstrate that IGVNS can efficiently compute the SBRP and achieve lower operational cost than the existing solutions. (c) 2020 Elsevier Ltd. All rights reserved.
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