Advertisement

Balancing Bicycle Sharing Systems: A Variable Neighborhood Search Approach

  • Marian Rainer-Harbach
  • Petrina Papazek
  • Bin Hu
  • Günther R. Raidl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7832)

Abstract

We consider the necessary redistribution of bicycles in public bicycle sharing systems in order to avoid rental stations to run empty or entirely full. For this purpose we propose a general Variable Neighborhood Search (VNS) with an embedded Variable Neighborhood Descent (VND) that exploits a series of neighborhood structures. While this metaheuristic generates candidate routes for vehicles to visit unbalanced rental stations, the numbers of bikes to be loaded or unloaded at each stop are efficiently derived by one of three alternative methods based on a greedy heuristic, a maximum flow calculation, and linear programming, respectively. Tests are performed on instances derived from real-world data and indicate that the VNS based on a greedy heuristic represents the best compromise for practice. In general the VNS yields good solutions and scales much better to larger instances than two mixed integer programming approaches.

Keywords

Mixed Integer Programming Variable Neighborhood Search Vehicle Rout Problem Greedy Heuristic Tour Length 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chemla, D., Meunier, F., Calvo, R.W.: Bike sharing systems: Solving the static rebalancing problem. To appear in Discrete Optimization (2012)Google Scholar
  2. 2.
    Raviv, T., Tzur, M., Forma, I.A.: Static Repositioning in a Bike-Sharing System: Models and Solution Approaches. To appear in EURO Journal on Transportation and Logistics (2012)Google Scholar
  3. 3.
    Benchimol, M., Benchimol, P., Chappert, B., De la Taille, A., Laroche, F., Meunier, F., Robinet, L.: Balancing the stations of a self service bike hire system. RAIRO – Operations Research 45(1), 37–61 (2011)zbMATHCrossRefGoogle Scholar
  4. 4.
    Contardo, C., Morency, C., Rousseau, L.M.: Balancing a Dynamic Public Bike-Sharing System. Technical Report CIRRELT-2012-09, CIRRELT, Montreal, Canada (2012), submitted to Transportation ScienceGoogle Scholar
  5. 5.
    Mladenović, N., Hansen, P.: Variable neighborhood search. Computers and Operations Research 24(11), 1097–1100 (1997)MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    Cherkassky, B.V., Goldberg, A.V.: On implementing the push-relabel method for the maximum flow problem. Algorithmica 19(4), 390–410 (1997)MathSciNetzbMATHCrossRefGoogle Scholar
  7. 7.
    Pirkwieser, S., Raidl, G.R.: A variable neighborhood search for the periodic vehicle routing problem with time windows. In: Prodhon, C., et al. (eds.) Proceedings of the 9th EU/MEeting on Metaheuristics for Logistics and Vehicle Routing, Troyes, France (2008)Google Scholar
  8. 8.
    Resende, M., Ribeiro, C.: Greedy randomized adaptive search procedures. In: Glover, F., Kochenberger, G. (eds.) Handbook of Metaheuristics, pp. 219–249. Kluwer Academic Publishers (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Marian Rainer-Harbach
    • 1
  • Petrina Papazek
    • 1
  • Bin Hu
    • 1
  • Günther R. Raidl
    • 1
  1. 1.Institute of Computer Graphics and AlgorithmsVienna University of TechnologyViennaAustria

Personalised recommendations