Published September 15, 2016 | Version v1
Journal article Open

Picking Routing Problem with K homogenous material handling equipment for a refrigerated warehouse

Authors/Creators

  • 1. Universidad Nacional de Colombia

Contributors

Description

This paper aims at formulating a Picking Routing Problem with K homogenous material handling equipment for a refrigerated warehouse (PRPHE). Discrete particle swarm optimization (PSO) and genetic algorithm (GA) metaheuristics are developed and
validated for solving PRPHE. The discrete PSO is a novel approach to solving cold routing picking problems, which has not been detected in the scientific literature and is considered a contribution to the state of the art. The main difference between classical and discrete PSO is the structure and algebraic formulation of the positions and velocities of the particles, which are discrete rather than continuous under our approach. A full factorial design was developed with the following four factors: picking routing metaheuristic (PRM), depot, picking list size (PLS) and homogeneous material handling equipment (MHE). Based on the results of the experimental analysis, we identified that GA metaheuristics generated better solutions than discrete PSO for PRPHE. These statistical results demonstrated that GA metaheuristics produced time savings of between 22.89 and 86.75 seconds per set of cold picking routes, as well as an increase in the operational efficiency of between 1.98 and 2.81%, as compared with PSO discrete. Finally, it should be noted that this paper is one of the first in tackling picking routing in a refrigerated warehouse, thereby contributing to knowledge in this field.

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References

  • 1. D. Battini, M. Calzavara, A. Persona and F. Sgarbossa, "A comparative analysis of different paperless picking systems", Industrial Management & Data Systems, vol. 115, no. 3, pp. 483-503, 2015
  • 2. D. Battini, M. Calzavara, A. Persona and F. Sgarbossa, "Order picking system design: the storage assignment and travel distance estimation (SA&TDE) joint method", International Journal of Production Research, vol. 53, no. 4, pp. 1077-1093, 2015.
  • 3. S. Henn, "Algorithms for on-line order batching in an order picking warehouse", Computers & Operations Research, vol. 39, no. 11, pp. 2549-2563, 2012.
  • 4. C. Ban et al., "Design of an Inventory Management System for Refrigerated Warehouses on Mobile Environments", in Future Information Communication Technology and Applications, 1st ed., H. Jung, J. Kim, T. Sahama and C. Yang (eds). Amsterdam, Netherlands: Springer, 2013, pp. 773-782.
  • 5. B. Menéndez, E. Pardo, A. Duarte, A. Ayuso and E. Molina, "General Variable Neighborhood Search applied to the picking process in a warehouse", Electronic Notes in Discrete Mathematics, vol. 47, pp. 77-84, 2015.
  • 6. O. Kulak, Y. Sahin and M. Taner, "Joint order batching and picker routing in single and multiple-crossaisle warehouses using cluster-based Tabu search algorithms", Flexible Services and Manufacturing Journal, vol. 24, no. 1, pp. 52-80, 2012.
  • 7. W. Lu, D. McFarlane, V. Giannikas and Q. Zhang, "An algorithm for dynamic order-picking in warehouse operations", European Journal of Operational Research, vol. 248, no. 1, pp. 107-122, 2016.
  • 8. J. Pan, P. Shih and M. Wu, "Order batching in a pickand- pass warehousing system with group genetic algorithm", Omega, vol. 57, pp. 238-248, 2015.
  • 9. A. Bonassa and C. Cunha, "The order-picking routing problem for low-level order picker in a warehouse", Gestão & Produção, vol. 18, no. 1, pp. 105-118, 2011.
  • 10. F. Chen, H. Wang, Y. Xie and C. Qi, "An ACO-based online routing method for multiple order pickers with congestion consideration in warehouse", Journal of Intelligent Manufacturing, vol. 27, no. 2, pp. 389-408, 2014.
  • 11. S. Henn and G. Wäscher, "Tabu search heuristics for the order batching problem in manual order picking systems", European Journal of Operational Research, vol. 222, no. 3, pp. 484-494, 2012.
  • 12. L. Liu, X. Gao and Q. Song, "Research of VRP Model with Semi-soft Time Window Constraints", Open Cybernetics & Systemics Journal, vol. 9, pp. 1083-1087, 2015.
  • 13. B. Yang et al., "Routing with time-windows for multiple environmental vehicle types", Computers & Industrial Engineering, vol. 89, pp. 150-161, 2015.
  • 14. M. Lin, K. Chin, K. Tsui and T. Wong, "Genetic based discrete particle swarm optimization for Elderly Day Care Center timetabling", Computers & Operations Research, vol. 65, pp. 125-138, 2016
  • 15. J. Kennedy and R. Eberhart, "A discrete binary version of the particle swarm algorithm", in International Conference on Systems, Man, and Cybernetics, Computational Cybernetics and Simulation, Orlando, USA, 1997, pp. 4104-4108.
  • 16. V. Kachitvichyanukul, P. Sombuntham and S. Kunnapapdeelert, "Two solution representations for solving multi-depot vehicle routing problem with multiple pickup and delivery requests via PSO", Computers & Industrial Engineering, vol. 89, pp. 125- 136, 2015.
  • 17. S. Talukder, "Mathematical modelling and applications of particle swarm optimization", M.S. thesis, Blekinge Institute of Technology, Karlskrona, Sweden, 2011.
  • 18. T. Vidal, T. Crainic, M. Gendreau and C. Prins, "A hybrid genetic algorithm with adaptive diversity management for a large class of vehicle routing problems with timewindows", Computers & Operations Research, vol. 40, no. 1, pp. 475-489, 2013
  • 19. A. Mohtashami, "A novel dynamic genetic algorithmbased method for vehicle scheduling in cross docking systems with frequent unloading operation", Computers & Industrial Engineering, vol. 90, pp. 221-240, 2015.
  • 20. J. Pan, P. Shih, M. Wu and J. Lin, "A storage assignment heuristic method based on genetic algorithm for a pickand- pass warehousing system", Computers & Industrial Engineering, vol. 81, pp. 1-13, 2015.