A branch-and-bound algorithm for solving the static rebalancing problem in bicycle-sharing systems

Kadri, Ahmed Abdelmoumene; Kacem, Imed; Labadi, Karim

doi:10.1016/j.cie.2016.02.002

Cited by 98 publications

(40 citation statements)

References 16 publications

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“…On the contrary, the dock availability (Ad, see (5)) decreases from 100% as the fleet size increases. This opposite evolution of the two curves creates a crossing point.…”

Section: Experiments and Resultsmentioning

confidence: 99%

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Performance Analysis and Improvement of the Bike Sharing System Using Closed Queuing Networks with Blocking Mechanism

et al. 2018

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The Bike Sharing System is a sustainable urban transport solution that consists of a fleet of bikes placed in various stations. Users will be satisfied if they find available bikes at their departure station and free docks at the destination. Despite the regulation operations of the system provider (i.e., redistribution of bikes by truck) deeper modifications (bike fleet size or station capacity) are often necessary to ensure a satisfactory service rate. In this paper, we model a sub-graph of a Bike Sharing System using the closed queuing network with a Repetitive-Service-Random-Destination blocking mechanism. This model is solved using the Maximum Entropy Method. This model faithfully reproduces the system dynamics considering the limited capacity of stations. We analyze the performance, particularly, via an overall performance indicator of the system. The various control and monitoring decisions (fleet-size, capacity of stations, incoming and outgoing flow of bikes) are applied to find out the best performance levels. The results demonstrate that the overall performance is robust enough regarding the fleet size changes but it degrades with the increase of the stations’ capacity. Finally, the arrival and departure flows control is an efficient and powerful operational leverage.

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“…On the contrary, the dock availability (Ad, see (5)) decreases from 100% as the fleet size increases. This opposite evolution of the two curves creates a crossing point.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…• Monitoring. The monitoring operations are real-time or short time regulation activities aiming to empty docks in the saturated stations and feeding the empty ones by relocating the bikes using trucks [5,6]. Alternatively, the operator offers financial incentives to the users, who then relocate the bikes themselves [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis and Improvement of the Bike Sharing System Using Closed Queuing Networks with Blocking Mechanism

et al. 2018

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show abstract

“…In terms of the number of trucks used in static BRP publications, both single and multiple vehicle problems are studied. However, compared with multiple vehicle BRP [5][6][7][8][9][10][11][12][14][15][16][17][18][19][20][21][22], it is more realistic to consider a single vehicle BRP for a part of the city BSS [3,4,[23][24][25][26][27][28][29][30]. In this paper, our study focuses on the single vehicle static BRP, which needs to be solved once at the beginning of every night, based on the status of the BSS at that time and the demand forecast for the next day and when the city is divided into districts.…”

Section: Literature Surveymentioning

confidence: 99%

A Bilevel Programming Model and Algorithm for the Static Bike Repositioning Problem

Tang

Zhuo

Qiu

2019

Journal of Advanced Transportation

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In this paper, by taking the outsourcing transportation mode into account, a bilevel programming model is proposed to formulate the static bike repositioning (SBR) problem, which can be used to determine the number of bikes loaded and unloaded at each station and the optimal truck routes in bike sharing systems (BSS). The upper-level BSS providers determine the optimal loading and unloading quantities at stations to minimize the total penalties. The lower-level truck owner pursues the minimum transportation route cost. An iterated local search and tabu search are developed to solve the model. Computational tests on a set of instances from 20 to 200 bikes demonstrate the effectiveness of the model and algorithms proposed, together with some insightful findings.

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“…Kadri et al. () tackle the problem by having the vehicle visit all stations exactly once. Target and initial inventory levels at the stations are known.…”

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

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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.

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A branch-and-bound algorithm for solving the static rebalancing problem in bicycle-sharing systems

Cited by 98 publications

References 16 publications

Performance Analysis and Improvement of the Bike Sharing System Using Closed Queuing Networks with Blocking Mechanism

Performance Analysis and Improvement of the Bike Sharing System Using Closed Queuing Networks with Blocking Mechanism

A Bilevel Programming Model and Algorithm for the Static Bike Repositioning Problem

Large neighborhood search for the bike request scheduling problem

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