On Solutions to Vehicle Routing Problems Using Swarm Optimization Techniques: A Review

  • Ashima GuptaEmail author
  • Sanjay Saini
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 553)


Vehicle Routing Problem (VRP) is among the intensively studied problem in the field of operations research. The literature of VRP has spread to dozens of variants that are studied till now, which makes the problem more complex. Due to its complexity and several real-time constraints, it is difficult to find optimal solutions for VRP models. In recent decades, swarm optimization techniques have emerged as promising solution to solve these problems optimally. The purpose of this research is to develop structural classification of different domains and attributes of VRP solved using swarm techniques. The findings of the study show the most studied attributes, capacitated VRP, time windows VRP, objective function with cost minimization and the least studied attributes, maximization objective function. The VRP literature is summarized in a manner that provides a clear view to identify future research directions.


Vehicle routing problem Meta-heuristic Swarm optimization 


  1. 1.
    Ai, J. and Kachitvichyanukul, V. A Particle Swarm Optimisation for Vehicle Routing Problem with Time Windows. International Journal of Operational Research, 56, 1 (2009), 519–537.Google Scholar
  2. 2.
    Ai, J. and Kachitvichyanukul, V. A particle swarm optimization for the vehicle routing problem with simultaneous pickup and delivery. Computers & Operations Research, 36, 5 (2009), 1693–1702.Google Scholar
  3. 3.
    Ai, J. and Kachitvichyanukul, V. A Study on Adaptive Particle Swarm Optimization for Solving Vehicle Routing Problems. In 9th Asia Pacific Industrial Engineering and Management Systems Conference (Bali, Indonesia 2008).Google Scholar
  4. 4.
    Ai, J. and Kachitvichyanukul, V. Particle swarm optimization and two solution representations for solving the capacitated VRP. Computers & Industrial Engineering, 56, 1 (2009), 380–387.Google Scholar
  5. 5.
    Ai-ling, Gen-ke, YANG, and Zhi-ming, WU. Hybrid discrete particle swarm optimization algorithm for capacitated vehicle routing problem. Journal of Zhejiang University SCIENCE A, 7, 4 (2006), 607–614.Google Scholar
  6. 6.
    Balseiro, S. R., Loiseau, I., and Ramone, J. An ant colony algorithm hybridized with insertion heuristics for the time dependent vehicle routing problem with time windows. Computers & Operations Research, 38 (2011), 954–966.Google Scholar
  7. 7.
    Bell, J E and McMullen, P R. Ant colony optimization techniques for the vehicle routing problem. Advanced Engineering Informatics (2004), 41–48.Google Scholar
  8. 8.
    Bin, Yu, Zhong-Zhen, Yang, and Baozhen, Yao. An improved ant colony optimization for vehicle routing problem. European Journal of Operational Research (2009), 171–176.Google Scholar
  9. 9.
    Bouhafs, Lyamine, Amir and Koukam, A. Hybrid Heuristic Approach to Solve the Capacitated Vehicle Routing Problem. Journal of Artificial Intelligence: Theory and Application, 1, 1 (2010), 31–34.Google Scholar
  10. 10.
    Doerner, K F, Hartl, R F, and Lucka, M. A parallel version of the D-Ant algorithm for the Vehicle Routing Problem, Parallel Numerics’ 05, (2005), 109–118.Google Scholar
  11. 11.
    Donati, V., Montemanni, R., Rizzoli, E., and Gambardella, M. Time Dependent VRP with Multi Ant Colony System. European Journal of Operational Research, 185, 3 (2008), 1174–1191.Google Scholar
  12. 12.
    Favaretto, D., Moretti, E., and Pellegrini, P. Ant colony system for a vrp with multiple time windows and multiple visits. Journal of Interdisciplinary Mathematics, 10, 2 (2007), 263–284.Google Scholar
  13. 13.
    Gambardella, L M, Rizzoli, A E, Oliverio, F, Donati, A V, Montemanni, R, and Lucibello, E. Ant Colony Optimization for vehicle routing in advanced logistics systems. In Proceedings of MAS 2003 – International Workshop on Modelling and Applied Simulation (Bergeggi, Italy 2003), 3–9.Google Scholar
  14. 14.
    Gambardella, L M, Taillard, E, and Agazzi, G. MACS-VRPTW: A Multiple Ant Colony System for Vehicle Routing Problems with Time Windows. In D. Corne and M. Dorigo, ed., New Ideas in Optimization. McGraw-Hill, UK, 1999.Google Scholar
  15. 15.
    Gendreau, M., Laporte, G., and Eguin, R. S. An exact algorithm for the vehicle routing problem with stochastic demands and customers. Transport. Sci., 29, 2 (1995), 143–155.Google Scholar
  16. 16.
    Gomez, A. and Salhi, S. Solving capacitated vehicle routing problem by artificial bee colony algorithm. Computational intelligence in Production and Logistics Systems (CIPLS) (2014), 48–52.Google Scholar
  17. 17.
    Hadjiconstantinou, E. and Roberts, D. Routing under uncertainity: an application in the scheduling of field service engineers. In Toth, P. and Vigo, D., eds., The vehicle routing problem. SIAM, (2001).Google Scholar
  18. 18.
    Iredi, S., Merkle, and Middendrof, M. Bi-Criterion Optimization with Multi Colony Ant System. Proc. International Conference on Evolutionary Multi-Criterion Optimization (EMO’01), (2001), 359–372.Google Scholar
  19. 19.
    Kanthavel, K. and Prasad, P. Optimization of Capacitated Vehicle Routing Problem by Nested Particle Swarm Optimization. American Journal of Applied Sciences, 8, 2 (2011), 107–112.Google Scholar
  20. 20.
    Lahyani, R., Khemakhan, M., and Semet, F. Rich vehicle routing problems: from a taxonomy to a definition. European Journal of Operation Research, 241, 1 (February 16 2015), 1–14.Google Scholar
  21. 21.
    Liu, X., Jiang, W., and Xie, J. Vehicle routing problem with time windows: a hybrid particle swarm optimization. Proc. Natural Computation, ICNC’09, 5th International Conference, (2009) 502–506.Google Scholar
  22. 22.
    Li, Z., and Zhao, F., Intelligent water drops algorithm for vehicle routing problem with time windows. In Proc. Service Systems & Service Management (ICSSSM), 11th International Conference, (2014), 1–6.Google Scholar
  23. 23.
    Manfrin, M. Ant Colony Optimization for the Vehicle Routing Problem. 2004.Google Scholar
  24. 24.
    Marinakis, Y., Mariniki, M., and Dounias, G. Honey Bee Mating Optimization Algorithm for the Vehicle Routing Problem. Studies in Computational Intelligence (SCI), 129 (2008), 139–148.Google Scholar
  25. 25.
    Masrom, S., Abidin, S. Z. Z., Nasir, A., and Rahman, A. Hybrid particle swarm optimization for vehicle routing problem with time windows. In Proceedings of the International Conference on Recent Researches in Computational Techniques, Non-Linear Systems and Control (2011), 142–147.Google Scholar
  26. 26.
    Parunak, H. Dyke and Brueckner, S. Engineering swarming systems. Methodologies and Software Engineering for Agent Systems (2004), 341–376.Google Scholar
  27. 27.
    Pellegrini, P., Favaretto, D., and Moretti, E. Multiple ant colony optimization for a rich VRP: a case study. In Knowledge-Based Intelligent Information and Engineering Systems, 4693 (2007), 627–634.Google Scholar
  28. 28.
    Ponce, Daniela. Bio-inspired Metaheuristics for the Vehicle Routing Problem. In Proceedings of the 9th WSEAS International Conference on APPLIED COMPUTER SCIENCE (), 80–84.Google Scholar
  29. 29.
    Reimann, M., Doerner, K., and Hartl, R. F. Insertion based ants for vehicle routing problems with backhauls and time windows. Ant Algorithms: Third International Working, (2002), 135–148.Google Scholar
  30. 30.
    Rizzoli, A E, Oliverio, F, Montemanni, R, and Gambardella, L M. Ant Colony Optimisation for vehicle routing problems: from theory to applications. Istituto Dalle Molle di Studi sull’Intelligenza, 2004.Google Scholar
  31. 31.
    Ruinelli, L. Column generation for a rich vrp: vrp with simultaneous distribution, collection and pickup-and-delivery. University of Applied Sciences and Arts, Southern Switzerland, 2011.Google Scholar
  32. 32.
    Saravanan, M and Sundararama, A. Ant colony optimization for one-sided time constraint vehicle routing problem. International Journal of Services, Economics & Management, 23–4 (2010), 332–349.Google Scholar
  33. 33.
    Snadhaya and Katiyar, V. An Enhanced Ant Colony System for Solving Vehicle Routing Problem with Time Window. international Journal of Computer Applications, 73, 12 (2013), 27–31.Google Scholar
  34. 34.
    Szeto, Y., and Ho, C. An artificial bee colony algorithm for the capacitated vehicle routing problem, (2011), 126–135.Google Scholar
  35. 35.
    Teymourian, E., Komaki, M., and Zandieh, M. Enhanced intelligent water drop and cuckoo search algorithms for solving the capacitated vehicle routing problem. Information Sciences, (2016), 354–378.Google Scholar
  36. 36.
    Ting., C. J. and Chen, H. Combination of multi ant colony and simulated annealing for the multi depot vehicle routing problem with time windows. Journal of Transportation Research Board (2009), 85–92.Google Scholar
  37. 37.
    Ting, C. J. and Chen, C. H. A multiple ant colony optimization algorithm for the capacitated location routing problem. International Journal of production Economics, 141, 1 (2013), 34–44.Google Scholar
  38. 38.
    Toth, P. and Vigo, D. The Vehicle Routing Problem. SIAM, 2001.Google Scholar
  39. 39.
    Yin, L. and Liu, X. A Single depot Complex Vehicle Routing Problem and its PSO Solution. Proc. Symposium on International Computer Science & Computational Technology (ICSCT) (2009),266–269.Google Scholar
  40. 40.
    Yu, B. and Yang, Z. Zhen. An Ant Colony Optimization: The Periodic Vehicle Routing Problem with Time Windows. Transportation Research: Logistics and Transportation Review, 47, 2 (2011), 166–181.Google Scholar
  41. 41.
    Zhang, X. and Tang, L. A new hybrid ant colony optimization algorithm for the vehicle routing problem. Pattern Recognition Letters, 30, 9 (2009), 848–855.Google Scholar
  42. 42.
    Zhen, T., and Zhang, Q., Hybrid Ant Colony Algorithm for the Vehicle Routing with Time Windows. Computing Communication, Control and Management, ISECS International Colloquium, (2008), 8–12.Google Scholar
  43. 43.
    Zhu, Q., Li, Y., and Zhu, S. An Improved Particle Swarm Optimization Algorithm for Vehicle Routing Problem with Time Windows. IEEE Conference on Evolutionary computation (Vancouver, 2006).Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Department of Physics & Computer ScienceDayalbagh Educational InstituteAgraIndia

Personalised recommendations