Balancing Bicycle Sharing Systems: A Variable Neighborhood Search Approach

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

doi:10.1007/978-3-642-37198-1_11

Cited by 98 publications

(72 citation statements)

References 7 publications

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“…As shown in previous work, this restriction has only a minimal impact on the theoretically achievable best solution quality [8].…”

Section: The Balancing Bicycle Sharing System Problemmentioning

confidence: 82%

“…In [8], we propose a greedy construction heuristic followed by a variable neighborhood search/variable neighborhood descent (VNS/VND) metaheuristic for efficiently finding vehicle routes. Three alternative auxiliary algorithms calculate meaningful loading instructions for given tours.…”

Section: Related Workmentioning

confidence: 99%

“…The greedy construction heuristic, which is in detail described in [8], sequentially constructs vehicle tours in a pure greedy manner following a local best successor strategy. First, we compute the maximum number of bicycles γ v that can be picked up or delivered at any station v in the set of feasible, i.e., not yet balanced, successor stations F:…”

Section: Greedy Construction Heuristicmentioning

confidence: 99%

“…The neighborhoods are described in detail in our previous work [8]: Remove station (REM-VND), insert unbalanced station (INS-U), replace station (REPL), intra or-opt (OR-OPT), 2-opt* inter-route exchange (2-OPT*), and intra-route 3-opt (3-OPT). All neighborhoods are searched using the best improvement strategy, and they are applied in random order, which turned out to work better in conjunction with PILOT-CH than the static order from [8].…”

Section: Variable Neighborhood Descentmentioning

confidence: 99%

See 3 more Smart Citations

A PILOT/VND/GRASP Hybrid for the Static Balancing of Public Bicycle Sharing Systems

Papazek

Raidl

Rainer-Harbach

et al. 2013

Lecture Notes in Computer Science

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Abstract. Due to varying user demands in bicycle sharing systems, operators need to actively shift bikes between stations by a fleet of vehicles. We address the problem of finding efficient vehicle tours by an extended version of an iterated greedy construction heuristic following the concept of the PILOT method and GRASP and applying a variable neighborhood descend (VND) as local improvement. Computational results on benchmark instances derived from the real-world scenario in Vienna with up to 700 stations indicate that our PILOT/GRASP hybrid especially scales significantly better to very large instances than a previously proposed variable neighborhood search (VNS) approach. Applying only one iteration, the PILOT construction heuristic followed by the VND provides good solutions very quickly, which can be potentially useful for urgent requests.

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“…As shown in previous work, this restriction has only a minimal impact on the theoretically achievable best solution quality [8].…”

Section: The Balancing Bicycle Sharing System Problemmentioning

confidence: 82%

Section: Related Workmentioning

confidence: 99%

Section: Greedy Construction Heuristicmentioning

confidence: 99%

Section: Variable Neighborhood Descentmentioning

confidence: 99%

See 2 more Smart Citations

A PILOT/VND/GRASP Hybrid for the Static Balancing of Public Bicycle Sharing Systems

Papazek

Raidl

Rainer-Harbach

et al. 2013

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…Particularly, [12,13,14] focus on the problem of finding routes at the end of the day for a set of carriers to achieve the desired configuration of bikes across the base stations. They have provided scalable exact and approximate approaches to this problem by either abstracting base stations into mega stations or by employing insights from inventory management or by using variable neighbourhood search based heuristics.…”

Section: Related Workmentioning

confidence: 99%

On-line Dynamic Station Redeployments in Bike-Sharing Systems

Manna

2016

AI*IA 2016 Advances in Artificial Intelligence

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Abstract. Bike-sharing has seen great development during recent years, both in Europe and globally. However, these systems are far from perfect. The uncertainty of the customer demand often leads to an unbalanced distribution of bicycles over the time and space (congestion and/or starvation), resulting both in a loss of customers and a poor customer experience. In order to improve those aspects, we propose a dynamic bike-sharing system, which combines the standard fixed base stations with movable stations (using trucks), which will able to be dynamically re-allocated according to the upcoming forecasted customer demand during the day in real-time. The purpose of this paper is to investigate whether using moveable stations in designing the bike-sharing system has a significant positive effect on the system performance. To that end, we contribute an on-line stochastic optimization formulation to address the redeployment of the moveable stations during the day, to better match the upcoming customer demand. Finally, we demonstrate the utility of our approach with numerical experiments using data provided by bike-sharing companies.

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The relocation problem for the one‐way electric vehicle sharing

2014

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Traditional car sharing services have been based on the two-way scheme, where the user picks up and returns the vehicle at the same parking station. Some innovative services permit also one-way trips, that is, the user is allowed to return the vehicle in another station. The one-way scheme is more attractive for the users, but may lead to an unbalance between the user demand, and the availability of vehicles or free lots at the stations. In such cases, the service provider could reallocate the fleet and restore a better distribution of the vehicles among the stations. In the case of electric car sharing, such a problem is more complex because the travel range depends on the level of the battery charge. This article presents a new approach for the relocation of electric vehicles (EVs), carried out by the staff of the service provider to keep the system balanced. Such an approach generates a challenging Paired Pickup and Delivery Problem with Time Windows with new features that to the best of our knowledge have never been considered in the literature. We call such a problem the EV relocation problem (EVRP). We yield a mixed integer linear programming (MILP) formulation of the EVRP and some techniques to speedup its solution through a state-of-the-art solver (CPLEX). Moreover, we develop a simple but effective heuristic based on such a formulation and four upper bound generation methods. We test the performances of both the MILP formulation and the heuristic on instances built on the Milan road network

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Balancing Bicycle Sharing Systems: A Variable Neighborhood Search Approach

Cited by 98 publications

References 7 publications

A PILOT/VND/GRASP Hybrid for the Static Balancing of Public Bicycle Sharing Systems

A PILOT/VND/GRASP Hybrid for the Static Balancing of Public Bicycle Sharing Systems

On-line Dynamic Station Redeployments in Bike-Sharing Systems

The relocation problem for the one‐way electric vehicle sharing

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