Balancing Bicycle Sharing Systems: Improving a VNS by Efficiently Determining Optimal Loading Operations

Raidl, Günther R.; Hu, Bin; Rainer-Harbach, Marian; Papazek, Petrina

doi:10.1007/978-3-642-38516-2_11

Cited by 30 publications

(22 citation statements)

References 9 publications

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“…The routing problem of each repositioning vehicle is then formulated as an integer program and solved by a commercial solver. Rainer-Harbach et al (2013) and Raidl et al (2013) present variable neighborhood search heuristics for a variant of the static repositioning problem that take the loading and unloading times into account.…”

Section: Literature Reviewmentioning

confidence: 99%

A 3-step math heuristic for the static repositioning problem in bike-sharing systems

Forma

Raviv

Tzur

2015

Transportation Research Part B: Methodological

234

144

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Section: Literature Reviewmentioning

confidence: 99%

A 3-step math heuristic for the static repositioning problem in bike-sharing systems

Forma

Raviv

Tzur

2015

Transportation Research Part B: Methodological

234

144

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“…In one stream, research is centered upon operational issues such as development models, network design, and vehicle scheduling from the program facilitator's point of view [9][10][11]. However, considering that the success of any bicycle sharing system is dependent upon consumer utilization [12], another stream of research is focused upon consumer usage.…”

Section: Introductionmentioning

confidence: 99%

Predicting Public Bicycle Adoption Using the Technology Acceptance Model

2015

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Abstract:Bicycle sharing programs provide a sustainable mode of urban transportation. Although cities across the globe have developed these systems for their citizens and visitors, usage rates are not as high as anticipated. This research uses the technology acceptance model as the basis to understand one's intention to adopt bicycle sharing programs. Using survey data derived from 421 participants in Beijing, China, the proposed covariance-based structural equation model consisting of perceived quality, perceived convenience, and perceived value is found to predict 50.5% of the variance in adoption intention. The findings of this research contribute to theory and practice in the burgeoning literature on public bicycle systems and sustainable urban transportation by offering a theoretical lens through which to consider system adoption, and providing information to practitioners as to what factors might contribute most to adoption.

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“…Papazek et al [36] have developed a PILOT heuristic [50] which improved the GCH from [39] significantly, a greedy randomized adaptive search procedure (GRASP) on both construction heuristics, performing very well on instances with a high number of rental stations. Raidl et al [38] examined different strategies for determining optimal loading and unloading decisions for given routes within a metaheuristic by specialized maximum-flow and linear programming approaches. Rainer-Harbach et al [40] refined their work on metaheuristics for the static case by providing comprehensive computational tests and have also introduced their time-indexed and hop-indexed MIP models.…”

Section: (Meta-)heuristics and Hybrid Approachesmentioning

confidence: 99%

“…The arborescence is realized by the single commodity flow conservation in Equations (34)- (38). According to (34) the amount of flow sent out from the depot at node 0 corresponds to the number of nodes assigned to vehicle l plus one to also reach 0 , that is, to get back to the depot.…”

Section: Master Problemmentioning

confidence: 99%

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

Kloimüllner

Raidl

2017

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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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Balancing Bicycle Sharing Systems: Improving a VNS by Efficiently Determining Optimal Loading Operations

Cited by 30 publications

References 9 publications

A 3-step math heuristic for the static repositioning problem in bike-sharing systems

A 3-step math heuristic for the static repositioning problem in bike-sharing systems

Predicting Public Bicycle Adoption Using the Technology Acceptance Model

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

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