Matheuristics for the Order-picking Problem with Sequence-dependant Constraints in a Logistic Center with a One-directional Conveyor Between Buffers
DOI:
https://doi.org/10.24425/mper.2024.149996Abstract
In the logistics center (warehouse or distribution center), customer orders need to be picked up by the pickers. In this research, we examine the order-picking problem with sequencedependent constraints with two decision variables (container start time and product quantity) in a distribution center with a one-directional conveyor. The decision-making is based on the developed two variations of two-step matheuristics. At first, the main order-picking problem is divided into two subproblems. Next, each step of each variant of the subproblem is solved using a mathematical programming-based technique. Both matheuristics were better in 85 of 120 test instances compared to the initial model solved by mathematical programming. Pickers matheuristics were better on average at 46.56%, while Buffers matheuristics were better on average at 46.87%. The proposed matheuristics approach allows distributors to schedule orders in the logistics center fast enough and with fewer resources.References
Beauchemin, M., Ménard, M.-A., Gaudreault, J., Lehoux, N., Agnard, S., & Quimper, C.-G. (2022). Dynamic allocation of human resources: case study in the metal 4.0 manufacturing industry. International Journal of Production Research, 1–17. DOI: 10.1080/ 00207543.2022.2139002.
Chiang, D.M.H., Lin, C.P., & Chen, M.C. (2011). The adaptive approach for storage assignment by mining data of warehouse management system for distribution centres. Enterprise Information Systems, 5 (2), 219–234. DOI: 10.1080/17517575.2010.537784.
Cinar, D., Oliveira, J.A., Ilker Topcu, Y., & Pardalos, P.M. (2017). Scheduling the truckload operations in automated warehouses with alternative aisles for pallets. Applied Soft Computing, 52, 566–574. DOI: 10.1016/J.ASOC.2016.10.013.
Czerniachowska, K., Wichniarek, R., & Żywicki, K. (2023a). A Model for an Order-Picking Problem with a One-Directional Conveyor and Buffer. Sustainability, 15 (18), 13731. DOI: 10.3390/su151813731.
Czerniachowska, K., Wichniarek, R., & Żywicki, K. (2023b). A Two-Step Matheuristics for Order-Picking Process Problems with One-Directional Material Flow and Buffers. Applied Sciences, 13 (18), 10099. DOI: 10.3390/app131810099.
Czerniachowska, K., Wichniarek, R., & Żywicki, K. (2023c). Constraint Programming for Flexible Flow Shop Scheduling Problem with Repeated Jobs and Repeated Operations. Advances in Science and Technology Research Journal, 17(3), 280–293. DOI: 10.12913/22998624/166588.
Da Col, G., & Teppan, E.C. (2019). Google vs IBM: A constraint solving challenge on the job-shop scheduling problem. Electronic Proceedings in Theoretical Computer Science, EPTCS, 306, 259–265. DOI: 10.4204/EPTCS.306.30.
Da Col, G., & Teppan, E.C. (2022). Industrial-size job shop scheduling with constraint programming. Operations Research Perspectives, 9, 100249. DOI: 10.1016/ j.orp.2022.100249.
Danilczuk, W., Gola, A., & Grznar, P. (2022). Job Scheduling Algorithm for a Hybrid MTO-MTS Production Process. IFAC-PapersOnLine, 55 (2). DOI: 10.1016/j.ifacol.2022.04.235.
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. DOI: 10.1016/j.ejor.2006.07.009.
Fuchigami, H.Y., & Rangel, S. (2018). A survey of case studies in production scheduling: Analysis and perspectives. Journal of Computational Science, 25, 425–436. DOI: 10.1016/j.jocs.2017.06.004.
Gu, J., Goetschalckx, M., & McGinnis, L.F. (2007). Research on warehouse operation: A comprehensive review. European Journal of Operational Research, 177 (1), 1–21. DOI: 10.1016/j.ejor.2006.02.025.
Haouassi, M., Kergosien, Y., Mendoza, J.E., & Rousseau, L.M. (2022). The integrated orderline batching, batch scheduling, and picker routing problem with multiple pickers: the benefits of splitting customer orders. Flexible Services and Manufacturing Journal, 34 (3), 614–645. DOI: 10.1007/s10696-021- 09425-8.
Hsu, S.Y., & Liu, C.H. (2009). Improving the delivery efficiency of the customer order scheduling problem in a job shop. Computers & Industrial Engineering, 57 (3), 856–866. DOI: 10.1016/J.CIE.2009.02.015.
Hultkrantz, O., & Lumsden, K. (2001). E-commerce and consequences for the logistics industry. Oecd the Impact of E-Commerce on Transport, 1–15.
Iwasaki, Y., Suzuki, I., Yamamoto, M., & Furukawa, M. (2013). Job-shop Scheduling Approach to Orderpicking Problem. Transactions of the Institute of Systems, Control and Information Engineers, 26 (3), 103– 109. DOI: 10.5687/iscie.26.103.
Kawęcki, N., & Gola, A. (2022). Pick Performance System as an IT Support for Order Completing – A Case Study. Lecture Notes in Mechanical Engineering, 105– 115. DOI: 10.1007/978-3-030-99310-8_9.
Kuthambalayan, T.S., & Bera, S. (2020). Managing product variety with mixed make-to-stock/maketo-order production strategy and guaranteed delivery time under stochastic demand. Computers and Industrial Engineering, 147, 106603. DOI: 10.1016/j.cie.2020.106603.
Lee, H.Y., & Murray, C.C. (2019). Robotics in order picking: evaluating warehouse layouts for pick, place, and transport vehicle routing systems. International Journal of Production Research, 57 (18), 5821–5841. DOI: 10.1080/00207543.2018.1552031.
Liu, C.M. (1999). Clustering techniques for stock location and order-picking in a distribution center. Computers and Operations Research, 26 (10–11), 989–1002. DOI: 10.1016/S0305-0548(99)00026-X.
Nowicki, E., & Smutnicki, C. (1996). A fast taboo search algorithm for the job shop problem. Management Science, 42 (6), 797–813. DOI: 10.1287/mnsc.42.6.797.
Onal, S., Zhu, W., & Das, S. (2023). Order picking heuristics for online order fulfillment warehouses with explosive storage. International Journal of Production Economics, 256, 108747. DOI: 10.1016/j.ijpe.2022.108747.
Peeters, K., & van Ooijen, H. (2020). Hybrid make-tostock and make-to-order systems: a taxonomic review. In International Journal of Production Research (Vol. 58, Issue 15, pp. 4659–4688). DOI: 10.1080/ 00207543.2020.1778204.
Petersen, C.G. (2000). An evaluation of order picking policies for mail order companies. Production and Operations Management, 9 (4), 319–335. DOI: 10.1111/ j.1937-5956.2000.tb00461.x.
Pinto, A.R.F., Nagano, M.S., & Boz, E. (2023). A classification approach to order picking systems and policies: Integrating automation and optimization for future research. Results in Control and Optimization, 12, 100281. DOI: 10.1016/j.rico.2023.100281.
Roodbergen, K.J., & De Koster, R. (2001). Routing methods for warehouses with multiple cross aisles. International Journal of Production Research, 39 (9), 1865–1883. DOI: 10.1080/00207540110028128.
Roodbergen, K.J., Vis, I.F.A., & Taylor, G.D. (2015). Simultaneous determination of warehouse layout and control policies. International Journal of Production Research, 53 (11), 3306–3326. DOI: 10.1080/00207543.2014.978029.
Roundy, R., Chen, D., Chen, P., Çakanyildirim, M., Freimer, M.B., & Melkonian, V. (2005). Capacitydriven acceptance of customer orders for a multi-stage batch manufacturing system: Models and algorithms. IIE Transactions (Institute of Industrial Engineers), 37 (12), 1093–1105. DOI: 10.1080/07408170500288042.
Sadeh, N., & Fox, M.S. (1996). Variable and value ordering heuristics for the job shop scheduling constraint satisfaction problem. Artificial Intelligence, 86 (1), 1–41. DOI: 10.1016/0004-3702(95)00098-4.
Tang, L.C., & Chew, E.P. (1997). Order picking systems : Batching and storage assignment strategies. Computers and Industrial Engineering, 33 (3–4), 817–820. DOI: 10.1016/s0360-8352(97)00245-3.
van Gils, T., Ramaekers, K., Caris, A., & de Koster, R.B.M. (2018). Designing efficient order picking systems by combining planning problems: State-of-the-art classification and review. European Journal of Operational Research. DOI: 10.1016/j.ejor.2017. 09.002.
Wu, Z.H., Chen, H.J., & Yang, J.J. (2020). Optimization of Order-Picking Problems by Intelligent Optimization Algorithm. Mathematical Problems in Engineering, 2020. DOI: 10.1155/2020/6352539.
