A Modular Soft Computing based Method for Vehicles Repositioning in Bike-sharing Systems

Caggiani, Leonardo; Ottomanelli, Michele

doi:10.1016/j.sbspro.2012.09.785

Cited by 77 publications

(36 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We note that our models can be parameterized to solve the target inventory problem. Caggiani and Ottomanelli (2012) construct a decision support system for routing. Chemla et al (2012) present a branch-and-cut algorithm for the single-vehicle problem, with results on instances of up to 100 stations.…”

Section: Related Workmentioning

confidence: 99%

Inventory rebalancing and vehicle routing in bike sharing systems

Schuijbroek

Hampshire

Hoeve

2017

European Journal of Operational Research

450

304

View full text Add to dashboard Cite

Bike sharing systems have been installed in many cities around the world and are increasing in popularity. A major operational cost driver in these systems is rebalancing the bikes over time such that the appropriate number of bikes and open docks are available to users. We combine two aspects that have previously been handled separately in the literature: determining service level requirements at each bike sharing station, and designing (near-)optimal vehicle routes to rebalance the inventory. Since finding provably optimal solutions is practically intractable, we propose a new cluster-first route-second heuristic, in which the polynomialsize Clustering Problem simultaneously considers the service level feasibility constraints and approximate routing costs. Extensive computational results on real-world data from Hubway (Boston, MA) and Capital Bikeshare (Washington, DC) are provided, which show that our heuristic outperforms a pure mixed integer programming formulation and a constraint programming approach.

show abstract

Section: Related Workmentioning

confidence: 99%

Inventory rebalancing and vehicle routing in bike sharing systems

Schuijbroek

Hampshire

Hoeve

2017

European Journal of Operational Research

450

304

View full text Add to dashboard Cite

show abstract

“…Caggiani and Ottomanelli (, ) tackle the online dynamic problem, where the problem context changes (e.g., due to stochastic demand), while implementing the solution and reoptimization can be necessary. Only a single vehicle is routed and routing is done by first performing pickups and then dropoffs.…”

Section: Literature Study On Bicycle Repositioningmentioning

confidence: 99%

Large neighborhood search for the bike request scheduling problem

Vergeylen

Sörensen

Vansteenwegen

2019

Int Trans Operational Res

View full text Add to dashboard Cite

This paper introduces an efficient algorithm for the bike request scheduling problem (BRSP). The BRSP is built around the concept of request, defined as the pickup or dropoff of a number of identical items (bikes) at a specific station, within a certain time window, and with a certain priority. The aim of the BRSP is to sequence requests on (and hence determine the routes of) a set of vehicles, in such a way that the sum of the priorities of the executed requests is maximized, all time windows are respected, and the capacity of the vehicles is not exceeded. The generation of the set of requests is explicitly not a part of the problem definition of the BRSP. The primary application of the BRSP, from which it derives its name, is to determine the routes of a set of repositioning vehicles in a bike sharing system, although other applications exist. The algorithm introduced in this paper is based on a set of related greedy randomized adaptive search procedure followed by variable neighborhood descent (GRASP + VND) operators embedded in a large neighborhood search (LNS) framework. Since this paper presents the first heuristic for the BRSP, a computational comparison to existing approaches is not possible. We therefore compare the solutions found by our LNS heuristic to those found by an exact solver (Gurobi). These experiments confirm that the proposed algorithm scales to realistic dimensions and is able to find near‐optimal solutions in seconds.

show abstract

“…At the tactical level we can refer to [30], [34], and [38] for analyses of the placement of bikes, while [20] studies the detection of broken bikes in the system. There are also a number of studies regarding the DBRP, see for example [1], [4], [5], [7], [21], [26], [27], [32], and [39]. An overview of planning problems arising in shared mobility systems, for example a bike-sharing system, is given in [22].…”

Section: Literature Surveymentioning

confidence: 99%

The Static Bicycle Repositioning Problem - Literature Survey and New Formulation

Espegren

Kristianslund

Andersson

et al. 2016

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. This paper considers the static bicycle repositioning problem (SBRP), which deals with optimally re-balancing bike sharing systems (BSS) overnight, i.e. using service vehicles to move bikes from (nearly) full stations to (nearly) empty stations. An exhaustive literature survey comparing existing models is presented, and a new and improved mathematical formulation for the SBRP is proposed. The model is tested on a number of instances generated based on data from a real BSS.

show abstract

A Modular Soft Computing based Method for Vehicles Repositioning in Bike-sharing Systems

Cited by 77 publications

References 12 publications

Inventory rebalancing and vehicle routing in bike sharing systems

Inventory rebalancing and vehicle routing in bike sharing systems

Large neighborhood search for the bike request scheduling problem

The Static Bicycle Repositioning Problem - Literature Survey and New Formulation

Contact Info

Product

Resources

About