Solving Stochastic Vehicle Routing Problem with Real Simultaneous Pickup and Delivery Using Differential Evolution

  • Eshetie Berhan
  • Pavel Krömer
  • Daniel Kitaw
  • Ajith Abraham
  • Václav Snášel
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 237)

Abstract

In this study, Stochastic VRP with Real Simultaneous Pickup and Delivery (SVRPSPD) is attempted the first time and fitted to a public transportation system in Anbessa City Bus Service Enterprise (ACBSE), Addis Ababa, Ethiopia. It is modeled and fitted with real data obtained from the enterprise. Due to its complexity, large instances of VRP and/or SVRPSPD are hard to solve using exact methods. Instead, various heuristic and metaheuristic algorithms are used to find feasible VRP solutions. In this work the Differential Evolution (DE) is used to optimize bus routes of ACBSE. The findings of the study shows that, DE algorithm is stable and able to reduce the estimated number of vehicles significantly. As compared to the traditional and exact algorithms it has exhibited better used fitness function.

Keywords

vehicle routing problem pickup and delivery machine learning differential evolution real-world application 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Christopher, G.J.: Solutions Methodologies for VRP with Stochastic Demand. Dessirtation, Iowa (2010)Google Scholar
  2. 2.
    Reimann, M., Doerner, K., Hartl, R.: D-ants: Savings based ants divide and conquer the vehicle routing problem. Computers & Operations Research 31(4), 563–591 (2003)CrossRefGoogle Scholar
  3. 3.
    Dantzig, G.B., Ramser, J.H.: The truck dispatching problem. Journal of Management Science, Management Science 6(1), 80–91 (1959)MathSciNetCrossRefMATHGoogle Scholar
  4. 4.
    Cordeau, J.F., Laporte, G., Mercier, A.: A unifid tabu search heuristic for vehicle routing problem with time windows. Journal of Operations Research Society 53, 928–936 (2001)CrossRefGoogle Scholar
  5. 5.
    Paolo, T., Daniele, V. (eds.): The Vehicle Routing Problem. SIAM Monographs on Discrete Mathematics and Applications. Society for Industrial and Applied Mathematics, Philadelphia (2002)Google Scholar
  6. 6.
    Dror, M., Trudeau, P.: Savings by split delivery routing. Transportation Science 23, 141–145 (1989)MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    Calvete, H.I., Carmen, G., María, J.O., Belén, S.-V.: Vehicle routing problems with soft time windows: an optimization based approach. Journal of Monografías del Seminario Matemático García de Galdeano 31, 295–304 (2004)Google Scholar
  8. 8.
    Bertsimas, D.: A vehicle routing problem with stochastic demand. Journal of Operations Research 40(3), 554–585 (1991)MathSciNetGoogle Scholar
  9. 9.
    Secomandi, N.: A rollout policy for the vehicle routing problem with stochastic demands. Operations Research 49, 796–802 (2001)CrossRefMATHGoogle Scholar
  10. 10.
    Laporte, G., Louveaux, F., van Hamme, L.: An integer l-shaped algorithm for the capacitated vehicle routing problem with stochastic demands. Operations Research 50, 415–423 (2002)MathSciNetCrossRefMATHGoogle Scholar
  11. 11.
    Gendreau, M., Laporte, G., Seguin, R.: Stochastic vehicle routing. European Journal of Operational Research 88, 3–12 (1996a)CrossRefMATHGoogle Scholar
  12. 12.
    Kenyon, A.S., Morton, D.P.: Stochastic vehicle routing with random travel times. Journal of Transportation Science 37(1), 69–82 (2003)CrossRefGoogle Scholar
  13. 13.
    Bertsimas, D.: A vehicle routing problem with stochastic demand. Operations Research 40, 574–585 (1992)MathSciNetCrossRefMATHGoogle Scholar
  14. 14.
    Reimann, M.: Analyzing a vehicle routing problem with stochastic demand using ant colony optimization. In: EURO Working Group on Transportation (2005)Google Scholar
  15. 15.
    Clarke, G., Wright, J.: Scheduling of vehicles from a central depot to a number of delivery points. Operations Research 12, 568–581 (1964)CrossRefGoogle Scholar
  16. 16.
    Ropke, S., Pisinger, D.: An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows. Transportation Science 40(4), 455–472 (2006)CrossRefGoogle Scholar
  17. 17.
    Linong, C.Y., Wan, R.I., Khairuddin, O., Zirour, M.: Vehicle routing problem: Models and solutions. Journal of Quality Measurement and Anlysis 4(1), 205–218 (2008)Google Scholar
  18. 18.
    Parragh, S., Doerner, K., Hartl, R.: A survey on pickup and delivery problems. Journal für Betriebswirtschaft 58, 81–117 (2008), 10.1007/s11301-008-0036-4Google Scholar
  19. 19.
    Kanthavel, K., Prasad, P.S.S., Vignesh, K.P.: Optimization of vehicle routing problem with simultaneous delivery and pickup using nested particle swarm optimization. European Journal of Scientific Research 73(3), 331–337 (2012)Google Scholar
  20. 20.
    Goksal, F.P., Karaoglan, I., Altiparmak, F.: A hybrid discrete particle swarm optimization for vehicle routing problem with simultaneous pickup and delivery. Computers & Industrial Engineering 65(1), 39–53 (2013)CrossRefGoogle Scholar
  21. 21.
    Liu, R., Xie, X., Augusto, V., Rodriguez, C.: Heuristic algorithms for a vehicle routing problem with simultaneous delivery and pickup and time windows in home health care. European Journal of Operational Research (2013)Google Scholar
  22. 22.
    Wang, H.F., Chen, Y.Y.: A genetic algorithm for the simultaneous delivery and pickup problems with time window. Computers & Industrial Engineering 62(1), 84–95 (2012)CrossRefGoogle Scholar
  23. 23.
    Karaoglan, I., Altiparmak, F., Kara, I., Dengiz, B.: The location-routing problem with simultaneous pickup and delivery: Formulations and a heuristic approach. Omega 40(4), 465–477 (2012)CrossRefGoogle Scholar
  24. 24.
    Zhang, T., Chaovalitwongse, W.A., Zhang, Y.: Scatter search for the stochastic travel-time vehicle routing problem with simultaneous pick-ups and deliveries. Computers & Operations Research 39(10), 2277–2290 (2012)MathSciNetCrossRefMATHGoogle Scholar
  25. 25.
    Cruz, R., Silva, T., Souza, M., Coelho, V., Mine, M., Martins, A.: Genvns-ts-cl-pr: A heuristic approach for solving the vehicle routing problem with simultaneous pickup and delivery. Electronic Notes in Discrete Mathematics 39, 217–224 (2012)CrossRefGoogle Scholar
  26. 26.
    Fermin, A.T., Roberto, D.G.: Vehicle routing problem with simultaneous pick-up and delivery service. Operational Research Society of India (OPSEARCH) 39(1), 19–34 (2002)MATHGoogle Scholar
  27. 27.
    Fermin, A.T., Roberto, D.G.: A tabu search algorithm for the vehicle routing problem with simultaneous pick-up and delivery service. Operational Research Society of India (OPSEARCH) 33(1), 595–619 (2006)MATHGoogle Scholar
  28. 28.
    Price, K.V., Storn, R.M., Lampinen, J.A.: Differential Evolution A Practical Approach to Global Optimization. Natural Computing Series. Springer, Berlin (2005)MATHGoogle Scholar
  29. 29.
    Storn, R., Price, K.: Differential Evolution- A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces. Technical report (1995)Google Scholar
  30. 30.
    Storn, R.: Differential evolution design of an IIR-filter. In: Proceeding of the IEEE Conference on Evolutionary Computation, ICEC, pp. 268–273. IEEE Press (1996)Google Scholar
  31. 31.
    Alba, E., Dorronsoro, B.: Solving the vehicle routing problem by using cellular genetic algorithms. In: Gottlieb, J., Raidl, G.R. (eds.) EvoCOP 2004. LNCS, vol. 3004, pp. 11–20. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  32. 32.
    Gendreau, M., Potvin, J.Y., Bräysy, O., Hasle, G., Løkketangen, A.: Metaheuristics for the vehicle routing problem and its extensions: A categorized bibliography. In: Golden, B., Raghavan, S., Wasil, E. (eds.) The Vehicle Routing Problem: Latest Advances and New Challenges. Operations Research/Computer Science Interfaces, vol. 43, pp. 143–169. Springer, US (2008)CrossRefGoogle Scholar
  33. 33.
    Hou, L., Hou, Z., Zhou, H.: Application of a novel discrete differential evolution algorithm to svrp. In: 2012 Fifth International Joint Conference on Computational Sciences and Optimization (CSO), pp. 141–145 (2012)Google Scholar
  34. 34.
    Liu, W., Wang, X., Li, X.: Memetic differential evolution for vehicle routing problem with time windows. In: Tan, Y., Shi, Y., Ji, Z. (eds.) ICSI 2012, Part I. LNCS, vol. 7331, pp. 358–365. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  35. 35.
    Xu, H., Wen, J.: Differential evolution algorithm for the optimization of the vehicle routing problem in logistics. In: 2012 Eighth International Conference on Computational Intelligence and Security (CIS), pp. 48–51 (2012)Google Scholar
  36. 36.
    Anbuudayasankar, S., Ganesh, K.: Mixed-integer linear programming for vehicle routing problem with simulatneous delivery and pick-up with maximum route-length. The International Journal of Applied Management and Technology 6(1), 31–52 (2008)Google Scholar
  37. 37.
    Krömer, P., Platoš, J., Snášel, V.: Modeling permutations for genetic algorithms. In: Proceedings of the International Conference of Soft Computing and Pattern Recognition (SoCPaR 2009), pp. 100–105. IEEE Computer Society (2009)Google Scholar
  38. 38.
    Snyder, L.V., Daskin, M.S.: A random-key genetic algorithm for the generalized traveling salesman problem. European Journal of Operational Research 174(1), 38–53 (2006)MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Eshetie Berhan
    • 1
  • Pavel Krömer
    • 2
    • 3
  • Daniel Kitaw
    • 1
  • Ajith Abraham
    • 3
  • Václav Snášel
    • 2
    • 3
  1. 1.School of Mechanical and Industrial EngineeringAddis Ababa University, Addis Ababa Institute of TechnologyAddis AbabaEthiopia
  2. 2.Faculty of Electrical Engineering and Computer ScienceVŠB Technical University of OstravaOstravaCzech Republic
  3. 3.IT4 InnovationsVŠB Technical University of OstravaOstravaCzech Republic

Personalised recommendations