An improved general variable neighborhood search for a static bike-sharing rebalancing problem considering the depot inventory. (1st December 2020)

• Record Type: Journal Article
• Authors: Ren, Yaping; Meng, Leilei; Zhao, Fu; Zhang, Chaoyong; Guo, Hongfei; Tian, Ying; Tong, Wen; Sutherland, John W.
• Is Part Of: Expert systems with applications. Volume 160(2020)
• Publication Date: 2020-12-01
• Subjects: Bike-sharing systems -- Rebalancing -- Depot inventory -- Variable neighborhood search; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2020.113752
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: The depot inventory is determined together with vehicle routes in static bike-sharing rebalancing problems. Two models are formulated to minimize the travel cost and the depot inventory cost. An improved general variable neighbourhood search is developed to solve our problem. Abstract: 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.