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Tackling a VRP challenge to redistribute scarce equipment within time windows using metaheuristic algorithms

  • Ahmed KheiriEmail author
  • Alina G. Dragomir
  • David Mueller
  • Joaquim Gromicho
  • Caroline Jagtenberg
  • Jelke J. van Hoorn
Research Paper
  • 65 Downloads

Abstract

This paper reports on the results of the VeRoLog Solver Challenge 2016–2017: the third solver challenge facilitated by VeRoLog, the EURO Working Group on Vehicle Routing and Logistics Optimization. The authors are the winners of second and third places, combined with members of the challenge organizing committee. The problem central to the challenge was a rich VRP: expensive and, therefore, scarce equipment was to be redistributed over customer locations within time windows. The difficulty was in creating combinations of pickups and deliveries that reduce the amount of equipment needed to execute the schedule, as well as the lengths of the routes and the number of vehicles used. This paper gives a description of the solution methods of the above-mentioned participants. The second place method involves sequences of 22 low level heuristics: each of these heuristics is associated with a transition probability to move to another low level heuristic. A randomly drawn sequence of these heuristics is applied to an initial solution, after which the probabilities are updated depending on whether or not this sequence improved the objective value, hence increasing the chance of selecting the sequences that generate improved solutions. The third place method decomposes the problem into two independent parts: first, it schedules the delivery days for all requests using a genetic algorithm. Each schedule in the genetic algorithm is evaluated by estimating its cost using a deterministic routing algorithm that constructs feasible routes for each day. After spending 80 percent of time in this phase, the last 20 percent of the computation time is spent on Variable Neighborhood Descent to further improve the routes found by the deterministic routing algorithm. This article finishes with an in-depth comparison of the results of the two approaches.

Keywords

Routing Evolutionary computations Metaheuristics Inventory 

Notes

Acknowledgements

We express our gratitude to the VeRoLog board as well as the organizing committee for the VeRoLog Conference that was held in Amsterdam, Netherlands, July 10–12, 2017. The last three authors wish to thank Gerhard Post and Daniël Mocking for co-organizing the VeRoLog Solver Challenge 2017. Alina G. Dragmir and David Mueller (team ADDM) have achieved their results for the all-time-best challenge using the Vienna Scientific Cluster. Additionally, Alina G. Dragomir would like to gratefully acknowledge the financial support by FWF the Austrian Science Fund (Project number P 27858).

References

  1. Ahmed L, Mumford C, Kheiri A (2019) Solving urban transit route design problem using selection hyper-heuristics. Eur J Oper Res 274(2):545–559Google Scholar
  2. Asta S, Özcan E (2015) A tensor-based selection hyper-heuristic for cross-domain heuristic search. Inf Sci 299:412–432Google Scholar
  3. Battarra M, Cordeau JF, Iori M (2014) Pickup-and-delivery problems for goods transportation. In: Toth P, Vigo D (eds) Vehicle Routing, chap 6, pp 161–191. doi:https://epubs.siam.org/doi/pdf/10.1137/1.9781611973594.ch6Google Scholar
  4. Burke EK, Gendreau M, Hyde M, Kendall G, Ochoa G, Özcan E, Qu R (2013) Hyper-heuristics: a survey of the state of the art. J Oper Res Soc 64(12):1695–1724Google Scholar
  5. Clarke G, Wright JW (1964) Scheduling of vehicles from a central depot to a number of delivery points. Oper Res 12(4):568–581Google Scholar
  6. Coelho LC, Cordeau JF, Laporte G (2014) Thirty years of inventory routing. Transp Sci 48(1):1–19Google Scholar
  7. Cordeau JF, Laporte G (2007) The dial-a-ride problem: models and algorithms. Ann Oper Res 153(1):29–46Google Scholar
  8. Croes GA (1958) A method for solving traveling-salesman problems. Oper Res 6(6):791–812Google Scholar
  9. Dethloff J (2001) Vehicle routing and reverse logistics: the vehicle routing problem with simultaneous delivery and pick-up. OR-Spektrum 23(1):79–96Google Scholar
  10. Dror M, Fortin D, Roucairol C (1998) Redistribution of self-service electric cars: a case of pickup and delivery. Tech. Rep. RR-3543, INRIAGoogle Scholar
  11. Dueck G (1993) New optimization heuristics: the great deluge algorithm and the record-to-record travel. J Comput Phys 104(1):86–92Google Scholar
  12. Dullaert W, Gromicho J, van Hoorn J, Post G, Vigo D (2017) The VeRoLog solver challenge 2016–2017. J Veh Rout Algorithms 1:1–3.  https://doi.org/10.1007/s41604-016-0001-7 Google Scholar
  13. Fagerland MW, Sandvik L (2009) The Wilcoxon–Mann–Whitney test under scrutiny. Stat Med 28(10):1487–1497Google Scholar
  14. Fishman E (2016) Bikeshare: a review of recent literature. Transp Rev 36(1):92–113Google Scholar
  15. Gromicho JA, Haneyah S, Kok L (2015) Solving a real-life vrp with inter-route and intra-route challenges.  https://doi.org/10.2139/ssrn.2610549
  16. Hansen P, Mladenović N (1999) An introduction to variable neighborhood search. In: Meta-heuristics, Springer, pp 433–458Google Scholar
  17. Hart E, Ross P, Corne D (2005) Evolutionary scheduling: a review. Genetic Progr Evol Mach 6(2):191–220Google Scholar
  18. Jian N, Freund D, Wiberg HM, Henderson SG (2016) Simulation optimization for a large-scale bike-sharing system. In: Proceedings of the 2016 Winter Simulation Conference, IEEE Press, Piscataway, NJ, USA, WSC ’16, pp 602–613Google Scholar
  19. Johnson DS (1973) Near-optimal bin packing algorithms. PhD thesis, Massachusetts Institute of TechnologyGoogle Scholar
  20. Kheiri A, Keedwell E (2015) A sequence-based selection hyper-heuristic utilising a hidden Markov model. In: Proceedings of the 2015 on genetic and evolutionary computation conference, GECCO ’15, pp 417–424Google Scholar
  21. Kheiri A, Keedwell E (2017) A hidden markov model approach to the problem of heuristic selection in hyper-heuristics with a case study in high school timetabling problems. Evolut Comput 25(3):473–501Google Scholar
  22. Kheiri A, Keedwell E, Gibson MJ, Savic D (2015) Sequence analysis-based hyper-heuristics for water distribution network optimisation. Procedia Engineering 119:1269–1277, computing and Control for the Water Industry (CCWI2015) Sharing the best practice in water managementGoogle Scholar
  23. Kruskal WH (1957) Historical notes on the Wilcoxon unpaired two-sample test. J Am Stat Assoc 52(279):356–360Google Scholar
  24. Lin S, Kernighan BW (1973) An effective heuristic algorithm for the traveling-salesman problem. Oper Res 21(2):498–516Google Scholar
  25. Martello S, Toth P (1990) Lower bounds and reduction procedures for the bin packing problem. Discrete Appl Math 28(1):59–70Google Scholar
  26. Masson R, Lehuede F, Peton O (2014) The dial-a-ride problem with transfers. Comput Oper Res 41:12–23Google Scholar
  27. Min H (1989) The multiple vehicle routing problem with simultaneous delivery and pick-up points. Transp Res Part A: General 23(5):377–386.  https://doi.org/10.1016/0191-2607(89)90085-X Google Scholar
  28. Montané FAT, Galvão RD (2006) A tabu search algorithm for the vehicle routing problem with simultaneous pick-up and delivery service. Comput OR 33:595–619Google Scholar
  29. Parragh S, Doerner K, Hartl R (2008) A survey on pickup and delivery problems: Part II: Transportation between pickup and delivery locations. Journal für Betriebswirtschaft 58:81–117.  https://doi.org/10.1007/s11301-008-0036-4 Google Scholar
  30. Pfrommer J, Warrington J, Schildbach G, Morari M (2014) Dynamic vehicle redistribution and online price incentives in shared mobility systems. IEEE Trans Intell Transp Syst 15(4):1567–1578Google Scholar
  31. Rainer-Harbach M, Papazek P, Hu B, Raidl GR (2013) Balancing bicycle sharing systems: a variable neighborhood search approach. In: Middendorf M, Blum C (eds) Evolutionary computation in combinatorial optimization. Springer, Berlin, pp 121–132Google Scholar
  32. Rand GK (2009) The life and times of the savings method for vehicle routing problems. ORiON 25(2):125–145Google Scholar
  33. Raviv T, Kolka O (2013) Optimal inventory management of a bike-sharing station. IIE Trans 45(10):1077–1093Google Scholar
  34. Schilde M, Doerner K, Hartl R (2011) Metaheuristics for the dynamic stochastic dial-a-ride problem with expected return transports. Comput Oper Res 38(12):1719–1730Google Scholar
  35. Schuijbroek J, Hampshire R, van Hoeve WJ (2017) Inventory rebalancing and vehicle routing in bike sharing systems. Eur J Oper Res 257(3):992–1004Google Scholar
  36. Sörensen K, Glover FW (2013) Metaheuristics. In: Gass SI, Fu MC (eds) Encyclopedia of operations research and management science. Springer, Berlin, pp 960–970Google Scholar
  37. Wall MB (1996) A genetic algorithm for resource-constrained scheduling. PhD thesis, Massachusetts Institute of TechnologyGoogle Scholar
  38. Wilson D, Rodrigues S, Segura C, Loshchilov I, Hutter F, Buenfil GL, Kheiri A, Keedwell E, Ocampo-Pineda M, Özcan E, Pena SIV, Goldman B, Rionda SB, Hernandez-Aguirre A, Veeramachaneni K, Cussat-Blanc S (2018) Evolutionary computation for wind farm layout optimization. Renew Energy 126:681–691Google Scholar

Copyright information

© The Association of European Operational Research Societies and Springer-Verlag GmbH Berlin Heidelberg 2019

Authors and Affiliations

  1. 1.Department of Management ScienceLancaster University Management SchoolLancasterUK
  2. 2.Faculty of Business, Economics and StatisticsUniversity of ViennaViennaAustria
  3. 3.Institute for Theoretical PhysicsVienna University of TechnologyViennaAustria
  4. 4.ORTECZoetermeerThe Netherlands
  5. 5.School of Business and EconomicsVrije UniversiteitAmsterdamThe Netherlands

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