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Journal of Intelligent Manufacturing

, Volume 30, Issue 8, pp 2927–2943 | Cite as

A hybrid meta-heuristic algorithm for vehicle routing and packing problem with cross-docking

  • İlker KüçükoğluEmail author
  • Nursel Öztürk
Article
  • 405 Downloads

Abstract

Cross-docking is a relatively new logistics strategy that has a great potential to eliminate storage cost and speed up the product flows. This paper considers the vehicle routing and packing problem with cross-docking and presents a mixed integer linear mathematical model. In the model, a set of trucks are used to transport products from suppliers to customers through cross-docking centers. Each supplier and customer node can be visited only once and directly shipping is not allowed from suppliers to customers. Moreover, truck capacities are identified with physical dimensional limits on the contrary of weight or amount of load. The objective of the study is to determine the vehicle routes that minimize the total distance. Due to the complexity of the mathematical model, a hybrid meta-heuristic algorithm (HMA), which integrates tabu search (TS) algorithm within simulated annealing (SA) algorithm, is proposed to solve the problem. Proposed HMA is tested on a well-known benchmark problem data set and compared with the SA and TS solutions. Results show that proposed HMA can produce effective solutions and outperforms the SA and TS especially for the large-sized problems.

Keywords

Hybrid meta-heuristic algorithm Cross-docking Vehicle routing 2-Dimensional vehicle loading 

References

  1. Agustina, D., Lee, C. K. M., & Piplani, R. (2014). Vehicle scheduling and routing at a cross docking center for food supply chains. International Journal of Production Economics, 152(1), 29–41.Google Scholar
  2. Arıkan, M., & Erol, S. (2012). A hybrid simulated annealing-tabu search algorithm for the part selection and machine loading problems in flexible manufacturing systems. The International Journal of Advanced Manufacturing Technology, 59(5–8), 669–679.Google Scholar
  3. Armentano, V. A., & Yamashita, D. S. (2000). Tabu search for scheduling on identical parallel machines to minimize mean tardiness. Journal of Intelligent Manufacturing, 11(5), 453–460.Google Scholar
  4. Azadeh, A., Farahani, M. H., Eivazy, H., Nazari-Shirkouhi, S., & Asadipour, G. (2013). A hybrid meta-heuristic algorithm for optimization of crew scheduling. Applied Soft Computing, 13(1), 158–164.Google Scholar
  5. Chen, C. S., Sarin, S., & Ram, B. (1991). The pallet packing problem for non-uniform box sizes. International Journal of Production Research, 29(10), 1963–1968.Google Scholar
  6. Dondo, R., & Cerdá, J. (2013). A sweep-heuristic based formulation for the vehicle routing problem with cross-docking. Computers and Chemical Engineering, 48(1), 293–311.Google Scholar
  7. Dondo, R., & Cerdá, J. (2014). A monolithic approach to vehicle routing and operations scheduling of a cross-dock system with multiple dock doors. Computers and Chemical Engineering, 63(1), 184–205.Google Scholar
  8. Dondo, R., Méndez, C. A., & Cerdá, J. (2011). The multi-echelon vehicle routing problem with cross docking in supply chain management. Computers and Chemical Engineering, 35(12), 3002–3024.Google Scholar
  9. Dong, M.-G., & Wang, N. (2012). A novel hybrid differential evolution approach to scheduling of large-scale zero-wait batch processes with setup times. Computers and Chemical Engineering, 45(1), 72–83.Google Scholar
  10. Fuellerer, G., Doerner, K. F., Hartl, R. F., & Iori, M. (2009). Ant colony optimization for the two-dimensional loading vehicle routing problem. Computers and Operations Research, 36(3), 655–673.Google Scholar
  11. Gendreau, M., Iori, M., Laporte, G., & Martello, S. (2006). A tabu search algorithm for a routing and container loading problem. Transportation Science, 40(3), 342–350.Google Scholar
  12. Gendreau, M., Iori, M., Laporte, G., & Martello, S. (2008). A tabu search heuristic for the vehicle routing problem with two-dimensional loading constraints. Networks, 51(1), 4–18.Google Scholar
  13. Geyik, F., & Cedimoglu, I. H. (2004). The strategies and parameters of tabu search for job-shop scheduling. Journal of Intelligent Manufacturing, 15(4), 439–448.Google Scholar
  14. Glover, F. (1989). Tabu search—Part I. ORSA Journal on Computing, 1(3), 190–206.Google Scholar
  15. Glover, F. (1990). Tabu search—Part II. ORSA Journal on Computing, 2(1), 4–32.Google Scholar
  16. Hamta, N., Ghomi, S. M. T. F., Tavakkoli-Moghaddam, R., & Jolai, F. (2014). A hybrid meta-heuristic for balancing and scheduling the assembly lines with sequence-independent setup times by considering deterioration tasks and learning effect. Scientia Iranica, Transactions E: Industrial Engineering, 21(3), 963–979.Google Scholar
  17. Hasani-Goodarzi, A., & Tavakkoli-Moghaddam, R. (2012). Capacitated vehicle routing problem for multi-product cross-docking with split deliveries and pickups. Procedia-Social and Behavioral Sciences, 62, 1360–1365.Google Scholar
  18. Hu, Z.-H., Zhao, Y., & Choi, T.-M. (2013). Vehicle routing problem for fashion supply chains with cross-docking. Mathematical Problems in Engineering, 2013, 1–10.Google Scholar
  19. Jolai, F., Rabiee, M., & Asefi, H. (2012). A novel hybrid meta-heuristic algorithm for a no-wait flexible flow shop scheduling problem with sequence dependent setup times. International Journal of Production Research, 50(24), 7447–7466.Google Scholar
  20. Katsigiannis, Y., Georgilakis, P. S., & Karapidakis, E. S. (2012). Hybrid simulated annealing-tabu search method for optimal sizing of autonomous power systems with renewables. IEEE Transactions on Sustainable Energy, 3(3), 330–338.Google Scholar
  21. Kaviani, M. A., Abbasi, M., Rahpeyma, B., & Yusefi, M. M. (2014). A hybrid tabu search-simulated annealing method to solve quadratic assignment problem. Decision Science Letters, 3(3), 391–396.Google Scholar
  22. Khebbache-Hadji, S., Prins, C., Yalaoui, A., & Reghioui, M. (2013). Heuristics and memetic algorithm for the two-dimensional loading capacitated vehicle routing problem with time windows. Central European Journal of Operations Research, 21(2), 307–336.Google Scholar
  23. Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simmulated annealing. Science, 220(4598), 671–680.Google Scholar
  24. Ladier, A.-L., & Alpan, G. (2014). Crossdock truck scheduling with time windows: Earliness, tardiness and storage policies. Journal of Intelligent Manufacturing, 1–15. doi: 10.1007/s10845-014-1014-4.Google Scholar
  25. Landrieu, A., Mati, Y., & Binder, Z. (2001). A tabu search heuristic for the single vehicle pickup and delivery problem with time windows. Journal of Intelligent Manufacturing, 12(5–6), 497–508.Google Scholar
  26. Lee, Y. H., Jung, J. W., & Lee, K. M. (2006). Vehicle routing scheduling for cross-docking in the supply chain. Computers and Industrial Engineering, 51(2), 247–256.Google Scholar
  27. Leno, I. J., Sankar, S. S., & Ponnambalam, S. G. (2015). MIP model and elitist strategy hybrid GA–SA algorithm for layout design. Journal of Intelligent Manufacturing, 1–19. doi: 10.1007/s10845-015-1113-x.Google Scholar
  28. Leung, S. C. H., Zhang, Z., Zhang, D., Hua, X., & Lim, M. K. (2013). A meta-heuristic algorithm for heterogeneous fleet vehicle routing problems with two-dimensional loading constraints. European Journal of Operational Research, 225(2), 199–210.Google Scholar
  29. Leung, S. C. H., Zhou, X., Zhang, D., & Zheng, J. (2011). Extended guided tabu search and a new packing algorithm for the two-dimensional loading vehicle routing problem. Computers and Operations Research, 38(1), 205–215.Google Scholar
  30. Liao, C.-J., Lin, Y., & Shih, S. C. (2010). Vehicle routing with cross-docking in the supply chain. Expert Systems with Applications, 37(10), 6868–6873.Google Scholar
  31. Lin, S.-W., Chou, S.-T., Lee, C.-Y., & Lee, Z.-J. (2012). A hybrid approach for vehicle routing problem with time windows. Advances in Intelligent Transportation Systems, 1(1), 11–18.Google Scholar
  32. Lin, S.-W., & Ying, K.-C. (2009). Applying a hybrid simulated annealing and tabu search approach to non-permutation flowshop scheduling problems. International Journal of Production Research, 47(5), 1411–1424.Google Scholar
  33. Moghadam, S. S., Ghomi, S. F., & Karimi, B. (2014). Vehicle routing scheduling problem with cross docking and split deliveries. Computers and Chemical Engineering, 69(1), 98–107.Google Scholar
  34. Morais, V. W. C., Mateus, G. R., & Noronha, T. F. (2014). Iterated local search heuristics for the vehicle routing problem with cross-docking. Expert Systems with Applications, 41(16), 7495–7506.Google Scholar
  35. Nearchou, A. C. (2004). Flow-shop sequencing using hybrid simulated annealing. Journal of Intelligent Manufacturing, 15(3), 317–328.Google Scholar
  36. Osman, I. H., & Christofides, N. (1994). Capacitated clustering problems by hybrid simulated annealing and tabu search. International Transactions in Operational Research, 1(3), 317–336.Google Scholar
  37. Otten, R. H. J. M., & Van Ginneken, L. P. P. P. (1988). Stop criteria in simulated annealing. IEEE International Conference on Computer Design: VLSI in Computers and Processors, 549–552. doi: 10.1109/ICCD.1988.25760.
  38. Poorzahedy, H., & Rouhani, O. M. (2007). Hybrid meta-heuristic algorithms for solving network design problem. European Journal of Operational Research, 182(2), 578–596.Google Scholar
  39. Ramezani, P., Rabiee, M., & Jolai, F. (2015). No-wait flexible flowshop with uniform parallel machines and sequence-dependent setup time: A hybrid meta-heuristic approach. Journal of Intelligent Manufacturing, 26(4), 731–744.Google Scholar
  40. Solomon, M. M. (1987). Algorithms for the vehicle routing and scheduling problems with time window constraints. Operations Research, 35(2), 254–265.Google Scholar
  41. Tan, K. C., Lee, L. H., Zhu, Q. L., & Ou, K. (2001). Heuristic methods for vehicle routing problem with time windows. Artificial intelligence in Engineering, 15(3), 281–295.Google Scholar
  42. Tarantilis, C. D. (2013). Adaptive multi-restart tabu search algorithm for the vehicle routing problem with cross-docking. Optimization Letters, 7(7), 1583–1596.Google Scholar
  43. Vahdani, B., & Sadigh, B. S. (2014). A multi-product vehicle routing scheduling model with time window constraints for cross docking system under uncertainty: A fuzzy possibilistic-stochastic programming. International Journal of Industrial Mathematics, 6(3), 215–228.Google Scholar
  44. Vahdani, B., Tavakkoli-Moghaddam, R., Zandieh, M., & Razmi, J. (2012). Vehicle routing scheduling using an enhanced hybrid optimization approach. Journal of Intelligent Manufacturing, 23(3), 759–774.Google Scholar
  45. Van Belle, J., Valckenaers, P., & Cattrysse, D. (2012). Cross-docking: State of the art. Omega, 40(6), 827–846.Google Scholar
  46. Wei, L., Oon, W.-C., Zhu, W., & Lim, A. (2011). A skyline heuristic for the 2D rectangular packing and strip packing problems. European Journal of Operational Research, 215(2), 337–346.Google Scholar
  47. Wei, L., Zhang, Z., Zhang, D., & Lim, A. (2015). A variable neighborhood search for the capacitated vehicle routing problem with two-dimensional loading constraints. European Journal of Operational Research, 243(3), 798–814.Google Scholar
  48. Wen, M., Larsen, J., Clausen, J., Cordeau, J.-F., & Laporte, G. (2009). Vehicle routing with cross-docking. Journal of the Operational Research Society, 60(12), 1708–1718.Google Scholar
  49. Zachariadis, E. E., Tarantilis, C. D., & Kiranoudis, C. T. (2009). A guided tabu search for the vehicle routing problem with two-dimensional loading constraints. European Journal of Operational Research, 195(3), 729–743.Google Scholar
  50. Zarandi, M. F., Khorshidian, H., & Shirazi, M. A. (2014). A constraint programming model for the scheduling of JIT cross-docking systems with preemption. Journal of Intelligent Manufacturing, 1–17. doi: 10.1007/s10845-013-0860-9.Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Industrial Engineering Department, Faculty of EngineeringUludag UniversityBursaTurkey

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