A heuristic algorithm for a single vehicle static bike sharing rebalancing problem. (March 2017)

• Record Type: Journal Article
• Authors: Cruz, Fábio; Subramanian, Anand; Bruck, Bruno P.; Iori, Manuel
• Is Part Of: Computers & operations research. Volume 79(2017)
• Page Start: 19
• Page End: 33
• Publication Date: 2017-03
• Subjects: Bike-sharing -- Vehicle routing -- Split pickup and delivery -- Iterated local search; Operations research -- Periodicals; Electronic digital computers -- Periodicals; 004.05
• DOI: 10.1016/j.cor.2016.09.025
• Languages: English
• ISSNs: 0305-0548

Abstract

Abstract: The static bike rebalancing problem (SBRP) concerns the task of repositioning bikes among stations in self-service bike-sharing systems. This problem can be seen as a variant of the one-commodity pickup and delivery vehicle routing problem, where multiple visits are allowed to be performed at each station, i.e., the demand of a station is allowed to be split. Moreover, a vehicle may temporarily drop its load at a station, leaving it in excess or, alternatively, collect more bikes from a station (even all of them), thus leaving it in default. Both cases require further visits in order to meet the actual demands of such station. This paper deals with a particular case of the SBRP, in which only a single vehicle is available and the objective is to find a least-cost route that meets the demand of all stations and does not violate the minimum (zero) and maximum (vehicle capacity) load limits along the tour. Therefore, the number of bikes to be collected or delivered at each station must be appropriately determined in order to respect such constraints. We propose an iterated local search (ILS) based heuristic to solve the problem. The ILS algorithm was tested on 980 benchmark instances from the literature and the results obtained are competitive when compared to other existing methods. Moreover, our heuristic was capable of finding most of the known optimal solutions and also of improving the results on a number of open instances. Abstract : Highlights: We propose an efficient hybrid heuristic algorithm for a single-vehicle SBRP. The heuristic found 97% of proven optimal solutions. The open instances were improved in 90% of the cases. Our algorithm outperformed the best known heuristics from the literature.