A Hybrid ACO+CP for Balancing Bicycle Sharing Systems

Gaspero, Luca Di; Rendl, Andrea; Urli, Tommaso

doi:10.1007/978-3-642-38516-2_16

Cited by 50 publications

(42 citation statements)

References 14 publications

(22 reference statements)

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“…Given the high degree of complexity, metaheuristics have been proposed to tackle larger instances and problems with multiple vehicles. We refer the interested reader to the heuristic papers by Rainer-Harbach, Papazek, Raidl, Hu, and Kloimüllner (2014), Papazek, Raidl, Rainer-Harbach, and Hu (2013), Gaspero, Rendl, and Urli (2013), and Schuijbroek, Hampshire, and van Hoeve (2013). In what follows, we will focus on the studies about exact methods for bicycle rebalancing problems.…”

Section: Literature Surveymentioning

confidence: 99%

An exact algorithm for the static rebalancing problem arising in bicycle sharing systems

Erdoğan

Battarra

Calvo

2015

European Journal of Operational Research

170

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a b s t r a c tBicycle sharing systems can significantly reduce traffic, pollution, and the need for parking spaces in city centers. One of the keys to success for a bicycle sharing system is the efficiency of rebalancing operations, where the number of bicycles in each station has to be restored to its target value by a truck through pickup and delivery operations. The Static Bicycle Rebalancing Problem aims to determine a minimum cost sequence of stations to be visited by a single vehicle as well as the amount of bicycles to be collected or delivered at each station. Multiple visits to a station are allowed, as well as using stations as temporary storage. This paper presents an exact algorithm for the problem and results of computational tests on benchmark instances from the literature. The computational experiments show that instances with up to 60 stations can be solved to optimality within 2 hours of computing time.

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Section: Literature Surveymentioning

confidence: 99%

An exact algorithm for the static rebalancing problem arising in bicycle sharing systems

Erdoğan

Battarra

Calvo

2015

European Journal of Operational Research

170

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“…There are a number of existing papers in the literature about the static case 85 (Benchimol et al [2], Chemla et al [3], Dell'Amico et al [4], Raviv et al [15,16]). …”

Section: Previous Workmentioning

confidence: 97%

“…In some cases, the perfect balance 100 requirement is a hard constraint ( [2], [3] requirement of each station by using a queuing system and Raviv and Kolka [17] compute a measure of dissatisfaction for different replenishment periods, given an initial inventory, the station size, and stochastic demand patterns. [15,16]), and matheuristics ( [18]). In three studies ( [4], [5], [19]), MIP formulations and/or relaxations of the problem are solved.…”

Section: Previous Workmentioning

confidence: 99%

“…These systems range from less than one hundred bicycles in small towns to many thousands in cities like Paris (20600), Hangzhou (78000), or Wuhan (90000). 15 The most important factor for the success of a public bike-sharing system is its ability to satisfy the varying demands of the users. Underlying the random variations of everyday demands, there are patterns of demand that have to be identified and estimated and the system has to be planned and managed to maximize the level of customer satisfaction.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing the level of service quality of a bike-sharing system

Álvarez-Valdés

Belenguer

Benavent

et al. 2016

Omega

166

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“…and is the primary objective of previous work such as [11,12,25,40,41]. In [40] the objective is particularly given as follows:…”

Section: Formal Problem Definitionmentioning

confidence: 99%

Full‐load route planning for balancing bike sharing systems by logic‐based benders decomposition

Kloimüllner

Raidl

2017

Networks

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Public bike sharing systems require some kind of rebalancing to avoid too many rental stations of running empty or entirely full, which would make the system ineffective and annoy customers. Most frequently, a fleet of vehicles with trailers is used for this purpose, moving bikes among the stations. Previous works considered different objectives and modeled the underlying routing problem in different ways, but they all allow an arbitrary number of bikes to be picked up at some stations and delivered to other stations, just limited by the vehicles' capacities. Observations in practice, however, indicate that in larger well-working bike sharing systems drivers almost never pickup or deliver only few bikes, but essentially always approximately full vehicle loads. Many stations even require several visits with full loads. Due to budgetary reasons, typically only just enough drivers and vehicles are employed to achieve a reasonable balance most of the time, but basically never an ideal one where single bikes play a substantial role. Consequently, we investigate here a simplified problem model, in which only full vehicle loads are considered for movement among the rental stations. This restriction appears to have only a minor impact on the achieved quality of the rebalancing in practice but eases the modeling substantially. More specifically, we formulate the rebalancing problem as a selective unit-capacity pickup and delivery problem with time budgets on a bipartite graph and present a compact mixed integer linear programming model, a logic-based Benders decomposition and a variant thereof, namely branch-and-check for it. For the general case, instances with up to 70 stations, and for the single-vehicle case instances with up to 120 stations are solved to proven optimality. A comparison to leading metaheuristic approaches considering flexible vehicle loads indicates that indeed the restriction to full loads has only a very small impact on the finally achieved balance in typical scenarios of Citybike Wien.

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A Hybrid ACO+CP for Balancing Bicycle Sharing Systems

Cited by 50 publications

References 14 publications

An exact algorithm for the static rebalancing problem arising in bicycle sharing systems

An exact algorithm for the static rebalancing problem arising in bicycle sharing systems

Optimizing the level of service quality of a bike-sharing system

Full‐load route planning for balancing bike sharing systems by logic‐based benders decomposition

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