Algoritmo evolutivo multiobjetivo basado en descomposición para la optimización del procesamiento por lotes de pedidos
Palabras clave:
metaheurísticas, algoritmo evolutivo, jobprpResumen
La demanda de prácticas logísticas sostenibles junto al auge del comercio electrónico, ha generado mayores exigencias en cuanto a la eficiencia y calidad en el procesamiento de pedidos. En este marco, y con el propósito de estudiar los métodos más adecuados para abordar el problema de agrupación y preparación de pedidos, se presenta una variante del JOBPRP con dos objetivos: los costos operativos y la distribución equilibrada de la carga de trabajo. En este contexto, los algoritmos evolutivos son buenas alternativas para la búsqueda multiobjetivo, pero pueden enfrentar obstáculos relacionados con la convergencia o la diversidad al abordar frentes de Pareto irregulares. Por esto se ha estudiado el desempeño del Algoritmo Evolutivo Multiobjetivo Basado en Descomposición, MOEA/D. Se realizó un análisis comparativo de su rendimiento utilizando diferentes métodos de escalarización en un conjunto exhaustivo de pruebas experimentales aplicadas a instancias de diferentes tamaños del problema abordado. Se emplearon como indicadores de desempeño el hipervolumen, la distancia promedio a la solución ideal y la dispersión de las soluciones no dominadas. Los resultados indican que el MOEA/D basado en el método de AASF ofrece un buen desempeño en términos de hipervolúmenes promedio y dispersión de soluciones a lo largo de los frentes.
ARK CAICYT: https://id.caicyt.gov.ar/ark:/s18539777/ok4i6st63
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Ardjmand, E., Shakeri, H., Singh, M., & Sanei Bajgiran, O. (2018). Minimizing order picking makespan with multiple pickers in a wave picking warehouse. International Journal of Production Economics, 206, 169-183.
https://doi.org/10.1016/j.ijpe.2018.10.001
Ardjmand, E., Youssef, E. M., Moyer, A., Young, W. A., Weckman, G. R., & Shakeri, H. (2020). A multi-objective model for minimising makespan and total travel time in put wall-based picking systems. International Journal of Logistics Systems and Management, 36(1), 138-176. https://doi.org/10.1504/IJLSM.2020.107230
Battini, D., Glock, C. H., Grosse, E. H., Persona, A., & Sgarbossa, F. (2016). Human energy expenditure in order picking storage assignment: A bi-objective method. Computers & Industrial Engineering, 94, 147-157.
https://doi.org/10.1016/j.cie.2016.01.020
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. https://doi.org/10.1007/s10845-016-1248-4
Chen, M.-C., & Wu, H.-P. (2005). An association-based clustering approach to order batching considering customer demand patterns. Omega, 33(4), 333-343.
https://doi.org/10.1016/j.omega.2004.05.003
Coello Coello, C. A. (2006). Evolutionary multi-objective optimization: A historical view of the field. IEEE Computational Intelligence Magazine, 1(1), 28-36. IEEE Computational Intelligence Magazine. https://doi.org/10.1109/MCI.2006.1597059
De Koster, M. B. M., Van der Poort, E. S., & Wolters, M. (1999). Efficient orderbatching methods in warehouses. International Journal of Production Research, 37(7), 1479-1504.
https://doi.org/10.1080/002075499191094
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. https://doi.org/10.1016/j.ejor.2006.07.009
Diefenbach, H., Emde, S., Glock, C. H., & Grosse, E. H. (2022). New solution procedures for the order picker routing problem in U-shaped pick areas with a movable depot. OR Spectrum, 44(2), 535-573. https://doi.org/10.1007/s00291-021-00663-8
Fang, K.-T., & Wang, Y. (1993). Number-theoretic methods in statistics (Vol. 51). CRC Press.
Grosse, E. H., & Glock, C. H. (2015). The effect of worker learning on manual order picking processes. International Journal of Production Economics, 170, 882-890.
https://doi.org/10.1016/j.ijpe.2014.12.018
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.
https://doi.org/10.1080/00207543.2016.1186296
Henn, S., Koch, S., & Wäscher, G. (2012). Order Batching in Order Picking Warehouses: A Survey of Solution Approaches. En R. Manzini (Ed.), Warehousing in the Global Supply Chain: Advanced Models, Tools and Applications for Storage Systems (pp. 105-137). Springer. https://doi.org/10.1007/978-1-4471-2274-6_6
Henn, S., & Schmid, V. (2013). Metaheuristics for order batching and sequencing in manual order picking systems. Computers & Industrial Engineering, 66(2), 338-351.
https://doi.org/10.1016/j.cie.2013.07.003
Ho, Y.-C., & Tseng, Y.-Y. (2006). A study on order-batching methods of order-picking in a distribution centre with two cross-aisles. International Journal of Production Research, 44(17), 3391-3417. https://doi.org/10.1080/00207540600558015
Hofmann, F. M., & Visagie, S. E. (2021). The Effect of Order Batching on a Cyclical Order Picking System. En M. Mes, E. Lalla-Ruiz, & S. Voß (Eds.), Computational Logistics (pp. 252-268). Springer International Publishing. https://doi.org/10.1007/978-3-030-87672-2_17
Karimi, N., Zandieh, M., & Karamooz, H. R. (2010). Bi-objective group scheduling in hybrid flexible flowshop: A multi-phase approach. Expert Systems with Applications, 37(6), 4024-4032. https://doi.org/10.1016/j.eswa.2009.09.005
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. https://doi.org/10.1007/s10696-011-9101-8
Lam, C. H. Y., Choy, K. L., Ho, G. T. S., & Lee, C. K. M. (2014). An order-picking operations system for managing the batching activities in a warehouse. International Journal of Systems Science, 45(6), 1283-1295. https://doi.org/10.1080/00207721.2012.761461
Miettinen, K. (1998). Nonlinear Multiobjective Optimization (Vol. 12). Springer US. https://doi.org/10.1007/978-1-4615-5563-6
Miguel, F., Frutos, M., Tohmé, F., & Rossit, D. (2019). A memetic algorithm for the integral OBP/OPP problem in a logistics distribution center. Uncertain Supply Chain Management, 7(2), 203-214.
Miguel, F. M., Frutos, M., Méndez, M., & Tohmé, F. (2021). Solving Order Batching/Picking Problems with an Evolutionary Algorithm. En D. A. Rossit, F. Tohmé, & G. Mejía Delgadillo (Eds.), Production Research (pp. 177-186). Springer International Publishing. https://doi.org/10.1007/978-3-030-76307-7_14
Miguel, F. M., Frutos, M., Méndez, M., Tohmé, F., & González, B. (2024). Comparison of MOEAs in an Optimization-Decision Methodology for a Joint Order Batching and Picking System. Mathematics, 12(8), Article 8. https://doi.org/10.3390/math12081246
Miguel, F. M., Frutos, M., Méndez, M., Tohmé, F., Miguel, F. M., Frutos, M., Méndez, M., & Tohmé, F. (2022). Order batching and order picking with 3D positioning of the articles: Solution through a hybrid evolutionary algorithm. Mathematical Biosciences and Engineering, 19(6), Article mbe-19-06-259.
https://doi.org/10.3934/mbe.2022259
Olmos, J., Florencia, R., García, V., González, M. V., Rivera, G., & Sánchez-Solís, P. (2022). Metaheuristics for Order Picking Optimisation: A Comparison Among Three Swarm-Intelligence Algorithms. En A. Ochoa-Zezzatti, D. Oliva, & A. E. Hassanien (Eds.), Technological and Industrial Applications Associated With Industry 4.0 (pp. 177-194). Springer International Publishing. https://doi.org/10.1007/978-3-030-68663-5_13
Pan, J. C.-H., Shih, P.-H., & Wu, M.-H. (2012). Storage assignment problem with travel distance and blocking considerations for a picker-to-part order picking system. Computers & Industrial Engineering, 62(2), 527-535.
https://doi.org/10.1016/j.cie.2011.11.001
Pardo, E. G., Gil-Borrás, S., Alonso-Ayuso, A., & Duarte, A. (2024). Order batching problems: Taxonomy and literature review. European Journal of Operational Research, 313(1), 1-24.
https://doi.org/10.1016/j.ejor.2023.02.019
Pescador-Rojas, M., & Coello, C. A. C. (2018). Collaborative and Adaptive Strategies of Different Scalarizing Functions in MOEA/D. 2018 IEEE Congress on Evolutionary Computation (CEC), 1-8. https://doi.org/10.1109/CEC.2018.8477815
Sancaklı, E., Dumlupınar, İ., Akçın, A. O., Çınar, E., Geylani, İ., & Düzgit, Z. (2022). Design of a Routing Algorithm for Efficient Order Picking in a Non-traditional Rectangular Warehouse Layout. En N. M. Durakbasa & M. G. Gençyılmaz (Eds.), Digitizing Production Systems (pp. 401-412). Springer International Publishing. https://doi.org/10.1007/978-3-030-90421-0_33
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. https://doi.org/10.1016/j.ejor.2017.04.038
Ten Hompel, M., & Schmidt, T. (2007). Warehouse Management. Springer. https://doi.org/10.1007/978-3-540-35220-4
Tsai, C.-Y., Liou, J. J. H., & Huang, T.-M. (2008). Using a multiple-GA method to solve the batch picking problem: Considering travel distance and order due time. International Journal of Production Research, 46(22), 6533-6555.
https://doi.org/10.1080/00207540701441947
van Gils, T., Ramaekers, K., Braekers, K., Depaire, B., & Caris, A. (2018). Increasing order picking efficiency by integrating storage, batching, zone picking, and routing policy decisions. International Journal of Production Economics, 197, 243-261. https://doi.org/10.1016/j.ijpe.2017.11.021
Vanheusden, S., Gils, T. van, Ramaekers, K., Cornelissens, T., & Caris, A. (2023). Practical factors in order picking planning: State-of-the-art classification and review. International Journal of Production Research.
https://doi.org/10.1080/00207543.2022.2053223
Wierzbicki, A. P. (1980). The Use of Reference Objectives in Multiobjective Optimization. En G. Fandel & T. Gal (Eds.), Multiple Criteria Decision Making Theory and Application (pp. 468-486). Springer. https://doi.org/10.1007/978-3-642-48782-8_32
Zhang, J., Wang, X., Chan, F. T. S., & Ruan, J. (2017). On-line order batching and sequencing problem with multiple pickers: A hybrid rule-based algorithm. Applied Mathematical Modelling, 45, 271-284. https://doi.org/10.1016/j.apm.2016.12.012
Zhang, Q., & Li, H. (2007). MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition. IEEE Transactions on Evolutionary Computation, 11(6), 712-731. IEEE Transactions on Evolutionary Computation.
https://doi.org/10.1109/TEVC.2007.892759
Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C. M., & da Fonseca, V. G. (2003). Performance assessment of multiobjective optimizers: An analysis and review. IEEE Transactions on Evolutionary Computation, 7(2), 117-132. IEEE Transactions on Evolutionary Computation. https://doi.org/10.1109/TEVC.2003.810758
Žulj, I., Glock, C. H., Grosse, E. H., & Schneider, M. (2018). Picker routing and storage-assignment strategies for precedence-constrained order picking. Computers & Industrial Engineering, 123, 338-347. https://doi.org/10.1016/j.cie.2018.06.015
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