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Manuscript ID : IMJ-2102-1546 (R1) Visit : 145 Page: 152 - 171

10.30495/imj.2021.686450

Article Type: Original Research

Model presentation to emptying the picking warehouse with heterogeneous containers in emergency situations with swarm intelligence algorithms

Subject Areas : Industrial Management

Amir Reza Ahmadi Keshavarz 1 , Davood Jaafari 2 , mehran khalaj 3 , Parshang Dokouhaki 4

1 - Department of Industrial Engineering, Parand Branch Islamic Azad University, Parand, Iran
2 - Department of Industrial Engineering, Parand Branch Islamic Azad University, Parand, Iran
3 - مدیر گروه
4 - Department of Industrial Engineering, Parand Branch Islamic Azad University, Parand, Iran

Received: 2021-02-03 Accepted : 2021-10-15 Published : 2021-11-20

Keywords: Swarm intelligence algorithms, Picking operations, Warehouse corridor congestion, Emergency logistics, Heterogeneous Courier containers,

Abstract :

Planning to empty warehouse cells is one of the most challenging issues in times of crisis. The need for emergency logistics for the efficient use of equipment is of great importance. In this study, a dual-objective planning model of routing and simultaneous scheduling of heterogeneous vehicles (picking containers)It has been suggested to evacuate the courier warehouse cells in emergency situations (non-compliance of the piece with the courier schedule) in order to minimize the movement time and maximize the reliability of the routes due to the congestion of the warehouse corridors. The developed Epsilon constraint method has been used to solve the proposed model. In the proposed model, the possibility of providing service for each warehouse cell that should be emptied by heterogeneous peak containers to logistics /warehouse areas with limited capacity is considered.To demonstrates the performance of the proposed model, the model is run on a random example and the computational results are presented. The results of problem-solving indicate a conflict between the objective functions used. In order to investigate the large-scale model, due to the Np-hard routing issues, three Particle Swarm Algorithms (PSO), Ant Colony (ACO), and Bee Colony (ABC), swarm intelligence algorithms were used and the results were compared with each other. The results of large-scale problem solving to find the best displacement path show better performance of the particle swarm algorithm.

References:


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    30.
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_||_

  1. Ahmadizar, F., Zeynivand, M., & Arkat, J. (2015). Two-level vehicle routing with cross-docking in a three-echelon supply chain: A genetic algorithm approach. Applied Mathematical Modelling, 39(22):7065-7081.
    2.
  2. Ardjmand, E., & Huh, D. W. (2017). Coordinated warehouse order picking and production scheduling: A nsgaii approach. In 2017 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1-8). IEEE.
    3.
  3. Ardjmand, E., Ghalehkhondabi, I., Young II, W. A., Sadeghi, A., Weckman, G. R., & Shakeri, H. (2020). A hybrid artificial neural network, genetic algorithm and column generation heuristic for minimizing makespan in manual order picking operations. Expert Systems with Applications, 159, 113566.
    4.
  4. Ardjmand, E., Shakeri, H., Singh, M., & Bajgiran, O. S. (2018). Minimizing order picking makespan with multiple pickers in a wave picking warehouse. International Journal of Production Economics, 206: 169-183.
    5.
  5. Cano, J. A., Correa-Espinal, A. A., & Gómez-Montoya, R. A. (2020). Mathematical programming modeling for joint order batching, sequencing and picker routing problems in manual order picking systems. Journal of King Saud University-Engineering Sciences, 32(3): 219-228.
    6.
  6. Cano, J.A., Cortés Achedad, P., Campo, E.A., & Correa Espinal, A.A. (2021). Solving the order batching and sequencing problem with multiple pickers: A grouped genetic algorithm. International Journal of Electrical and Computer Engineering (IJECE), 11(3): 2516-2524.
    7.
  7. Cergibozan, Ç. & Tasan, A. S. (2019). Order batching operations: an overview of classification, solution techniques, and future research. Journal of Intelligent Manufacturing, 30(1): 335-349.
    8.
  8. Chen, T. L., Cheng, C. Y., Chen, Y. Y., & Chan, L. K. (2015). An efficient hybrid algorithm for integrated order batching, sequencing and routing problem. International Journal of Production Economics, 159: 158-167.
    9.
  9. Cheng, C. Y., Chen, Y. Y., Chen, T. L., & Yoo, J. J. W. (2015). Using a hybrid approach based on the particle swarm optimization and ant colony optimization to solve a joint order batching and picker routing problem. International Journal of Production Economics, 170: 805-814.
    10.
  10. Cortés, P., Gómez-Montoya, R. A., Muñuzuri, J., & Correa-Espinal, A. (2017). A tabu search approach to solving the picking routing problem for large-and medium-size distribution centres considering the availability of inventory and K heterogeneous material handling equipment. Applied Soft Computing, 53: 61-73.
    11.
  11. De Koster, R., Le-Duc, T., & Roodbergen, K. J. (2007). Design and control of warehouse order picking: A literature review. European journal of operational research, 182(2):, 481-501.
    12.
  12. Dorigo, M., Bonabeau, E., & Theraulaz, G. (2000). Ant algorithms and stigmergy. Future Generation Computer Systems, 16(8): 851-871.
    13.
  13. Duan, Q., & Liao, T. W. (2014). Optimization of blood supply chain with shortened shelf lives and ABO compatibility. International Journal of Production Economics, 153: 113-129.
    14.
  14. Feng, X., & Hu, X. (2021). A Heuristic Solution Approach to Order Batching and Sequencing for Manual Picking and Packing Lines considering Fatiguing Effect. Scientific Programming, 2021.
    15.
  15. Glock, C. H., Grosse, E. H., Elbert, R. M., & Franzke, T. (2017). Maverick picking: the impact of modifications in work schedules on manual order picking processes. International Journal of Production Research, 55(21): 6344-6360.
    16.
  16. Grosse, E. H., Glock, C. H., & Müller, S. (2015). Production economics and the learning curve: A meta-analysis. International Journal of Production Economics, 170: 401-412.
    17.
  17. Grosse, E. H., Glock, C. H., & Neumann, W. P. (2017). Human factors in order picking: a content analysis of the literature. International Journal of Production Research, 55(5): 1260-1276.
    18.
  18. Gupta, S., Starr, M. K., Farahani, R. Z., & Matinrad, N. (2016). Disaster management from a POM perspective: Mapping a new domain. Production and Operations Management, 25(10): 1611-1637.
    19.
  19. Isler, C. A., Righetto, G. M., & Morabito, R. (2016). Optimizing the order picking of a scholar and office supplies warehouse. The International Journal of Advanced Manufacturing Technology, 87(5): 2327-2336.
    20.
  20. Karaboga, D., & Akay, B. (2009). A comparative study of artificial bee colony algorithm. Applied mathematics and computation, 214(1): 108-132.
    21.
  21. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN'95-international conference on neural networks .Vol. 4: 1942-1948). IEEE.
    22.
  22. Kübler, P., Glock, C. H., & Bauernhansl, T. (2020). A new iterative method for solving the joint dynamic storage location assignment, order batching and picker routing problem in manual picker-to-parts warehouses. Computers & Industrial Engineering, 147: 106645.
    23.
  23. Kulak, O., Sahin, Y., & Taner, M. E. (2012). Joint order batching and picker routing in single and multiple-cross-aisle warehouses using cluster-based tabu search algorithms. Flexible services and manufacturing journal, 24(1): 52-80.
    24.
  24. Lei, F., & Jianmin, H. (2003). Model and algorithm for optimal selection of emergency system. Systems Engineering-theory and Practice, 18(1): 49-54.
    25.
  25. Li, J., Huang, R., & Dai, J. B. (2017). Joint optimisation of order batching and picker routing in the online retailer’s warehouse in China. International Journal of Production Research, 55(2): 447-461.
    26.
  26. Lin, C. C., Kang, J. R., Hou, C. C., & Cheng, C. Y. (2016). Joint order batching and picker Manhattan routing problem. Computers & Industrial Engineering, 95: 164-174.
    27.
  27. Matusiak, M., de Koster, R., Kroon, L., & Saarinen, J. (2014). A fast simulated annealing method for batching precedence-constrained customer orders in a warehouse. European Journal of Operational Research, 236(3): 968-977.
    28.
  28. Mavrotas, G. (2009). Effective implementation of the ε-constraint method in multi-objective mathematical programming problems. Applied mathematics and computation, 213(2): 455-465.
    29.
  29. Menéndez, B., Bustillo, M., Pardo, E. G., & Duarte, A. (2017a). General variable neighborhood search for the order batching and sequencing problem. European Journal of Operational Research, 263(1): 82-93.
    30.
  30. Menéndez, B., Pardo, E. G., Alonso-Ayuso, A., Molina, E., & Duarte, A. (2017b). Variable neighborhood search strategies for the order batching problem. Computers & Operations Research, 78: 500-512.
    31.
  31. Menendez, B., Pardo, E. G., Sánchez‐Oro, J., & Duarte, A. (2017c). Parallel variable neighborhood search for the min–max order batching problem. International Transactions in Operational Research, 24(3): 635-662.
    32.
  32. Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software, 95: 51-67.
    33.
  33. Mousavi, S. M., Vahdani, B., Tavakkoli-Moghaddam, R., & Hashemi, H. (2014). Location of cross-docking centers and vehicle routing scheduling under uncertainty: a fuzzy possibilistic–stochastic programming model. Applied Mathematical Modelling, 38(7-8): 2249-2264.
    34.
  34. Pansart, L., Catusse, N., & Cambazard, H. (2018). Exact algorithms for the order picking problem. Computers & Operations Research, 100: 117-127.
    35.
  35. Scholz, A., & Wäscher, G. (2017). Order Batching and Picker Routing in manual order picking systems: the benefits of integrated routing. Central European Journal of Operations Research, 25(2): 491-520.
    36.
  36. Scholz, A., Schubert, D., & Wäscher, G. (2017). Order picking with multiple pickers and due dates–simultaneous solution of order batching, batch assignment and sequencing, and picker routing problems. European Journal of Operational Research, 263(2): 461-478.
    37.
  37. Valle, C. A., & Beasley, J. E. (2020). Order batching using an approximation for the distance travelled by pickers. European Journal of Operational Research, 284(2): 460-484.
    38.
  38. Valle, C. A., Beasley, J. E., & Da Cunha, A. S. (2017). Optimally solving the joint order batching and picker routing problem. European Journal of Operational Research, 262(3): 817-834.
    39.
  39. Van Gils, T., Caris, A., Ramaekers, K., & Braekers, K. (2019). Formulating and solving the integrated batching, routing, and picker scheduling problem in a real-life spare parts warehouse. European Journal of Operational Research, 277(3): 814-830.
    40.
  40. Van Gils, T., Ramaekers, K., Caris, A., & de Koster, R. B. (2018). Designing efficient order picking systems by combining planning problems: State-of-the-art classification and review. European Journal of Operational Research, 267(1): 1-15.
    41.
  41. Vanheusden, S., van Gils, T., Braekers, K., Ramaekers, K., & Caris, A. (2021). Analysing the effectiveness of workload balancing measures in order picking operations. International Journal of Production Research, 1-25.
    42.
  42. Yousefikhoshbakht, M., Didehvar, F., & Rahmati, F. (2014). Solving the heterogeneous fixed fleet open vehicle routing problem by a combined metaheuristic algorithm. International Journal of Production Research, 52(9): 2565-2575.
    43.

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