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A Hybrid and Adaptive Metaheuristic for the Rebalancing Problem in Public Bicycle Systems

  • Haitao Xu
  • Jing Ying
Article
  • 30 Downloads

Abstract

To meet the fluctuating demand for bicycles and for vacant lockers at each station, employees need to actively shift bicycles between stations by a fleet of vehicles. This is the rebalancing problem in public bicycle systems. In this paper, we propose a new objective function to the rebalancing problem, which meets the actual circs better. Then we explore a new method combines data mining process with GRASP-PR which incorporate GRASP and path-relinking procedure to experiment, not a single activation, but multiple and adaptive executions of the data mining process during the metaheuristic execution. And some improvements are made in some phases of the algorithm according to the feature of the bicycle rebalancing problem. Practice examples and comparison with the typical algorithm in the fields are made. The results show that the new proposals were able to find better results in less computational time for the rebalancing bicycle problem. The research result has been implemented in Hangzhou, China.

Keywords

Public bicycle system Greedy randomized adaptive search procedure Path-relinking Data mining Bicycle rebalancing 

Notes

Acknowledgements

This work was financially supported by Chinese National Natural Science Foundation(61572165) and Public Projects of Zhejiang Province(LGF18F030006).

References

  1. 1.
    Chemla, D., Meunier, F., Calvo, R.W.: Bike sharing systems: solving the static rebalancing problem. Discret. Optim. 10(2), 120–146 (2013)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Demaio, P.: Bike-sharing: history, impacts, models of provision, and future. J Public Transp. 12(4), 41–56 (2009)CrossRefGoogle Scholar
  3. 3.
    Ho, S.C., Szeto, W.Y.: A hybrid large neighborhood search for the static multi-vehicle bike-repositioning problem. Transp. Res. B Methodol. 95, 340–363 (2017)CrossRefGoogle Scholar
  4. 4.
    Ho, S.C., Szeto, W.Y.: Solving a static repositioning problem in bike-sharing systems using iterated tabu search. Transp. Res. E. 69(3), 180–198 (2014)CrossRefGoogle Scholar
  5. 5.
    Erdoğan, G., Laporte, G., & Calvo, R. W. (2013). The one commodity pickup and delivery traveling salesman problem with demand intervals. Technical Report CIRRELT-2013-46, MontrealGoogle Scholar
  6. 6.
    Rainer-Harbach, M., Papazek, P., Hu, B., Raidl, G.R.: Balancing bicycle sharing systems: a variable neighborhood search approach. European conference on evolutionary computation in combinatorial optimization, vol. 7832, pp. 121–132. Springer-Verlag, Heidelberg (2013)CrossRefGoogle Scholar
  7. 7.
    Raidl, G.R., Hu, B., Rainer-Harbach, M., Papazek, P.: Balancing bicycle sharing systems: improving a VNS by efficiently determining optimal loading operations. International workshop on hybridmetaheuristics, vol. 7919, pp. 130–143. Springer, Berlin, Heidelberg (2013)CrossRefGoogle Scholar
  8. 8.
    Papazek, P., Kloimüllner, C., Hu, B., Raidl, G.R.: Balancing bicycle sharing systems: an analysis of path relinking and recombination within a GRASP hybrid. Parallel Problem Solving from Nature – PPSN XIII. 8672, 792–801 (2014)Google Scholar
  9. 9.
    Forma, I.A., Raviv, T., Tzur, M.: A 3-step math heuristic for the static repositioning problem in bike-sharing systems. Transp. Res. B Methodol. 71, 230–247 (2015)CrossRefGoogle Scholar
  10. 10.
    Talbi, E.G.: A taxonomy of hybrid metaheuristics. J. Heuristics. 8(5), 541–564 (2002)CrossRefGoogle Scholar
  11. 11.
    Plastino, A., Barbalho, H., Santos, L.F.M., Fuchshuber, R., Martins, S.L.: Adaptive and multi-mining versions of the dm-grasp hybrid metaheuristic. J. Heuristics. 20(1), 39–74 (2014)CrossRefGoogle Scholar
  12. 12.
    Schuijbroek, J., Hampshire, R.C., Hoeve, W.J.V.: Inventory rebalancing and vehicle routing in bike sharing systems. Eur. J. Oper. Res. 257(3), 992–1004 (2017)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Barbalho, H., Rosseti, I., Martins, S.L., Plastino, A.: A hybrid data mining grasp with path-relinking. Comput. Oper. Res. 40(12), 3159–3173 (2013)CrossRefGoogle Scholar
  14. 14.
    Martí, R., Campos, V., Resende, M.G.C., Duarte, A.: Multiobjective grasp with path relinking. Eur. J. Oper. Res. 240(1), 54–71 (2015)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Ribeiro, C.C., Rosseti, I.: Efficient parallel cooperative implementations of grasp heuristics. Parallel Comput. 33(1), 21–35 (2007)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Feo, T.A., Resende, M.G.C.: Greedy randomized adaptive search procedures. J. Glob. Optim. 6(2), 109–133 (1995)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Angel-Bello, F.R., González-Velarde, J.L., Alvarez, A.M.: Greedy randomized adaptive search procedures. Metaheuristic Procedures for Training Neutral Networks, pp. 207–223. Springer (2006)Google Scholar
  18. 18.
    Glover, F., Laguna, M., Marti, R.: Scatter search and path relinking: advances and applications. Handbook of Metaheuristics, pp. 1–35. Kluwer Academic Publishers (2003)Google Scholar
  19. 19.
    Glover, F.: Multi-start and strategic oscillation methods — principles to exploit adaptive memory. Computing Tools for Modeling, Optimization and Simulation, pp. 1–23. Springer, Boston (2000)Google Scholar
  20. 20.
    Aiex, R.M., Resende, M.G.C., Pardalos, P.M., Toraldo, G.: GRASP with path-relinking for the three-index assignment problem. INFORMS J. Comput. 17(2), 224–247 (2005)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Glover, F.: Tabu search and adaptive memory programming — advances, applications and challenges. Interfaces in Computer Science and Operations Research, pp. 1–75. Springer (1997)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyZhejiang UniversityHangzhouChina
  2. 2.College of Computer Science and TechnologyHangzhou Dianzi UniversityHangzhouChina

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