Çok Depotlu Manuel Depolarda Sipariş Gruplama, Depot Atama ve Rotalama Problemini Çözmek için Aglomeratif Koridor Kümeleme Yaklaşımı

Yıl 2025, Cilt: 36 Sayı: 2, 207 - 235, 31.08.2025

Selma Gülyeşil , Zeynep Didem Unutmaz Durmuşoğlu

Öz

Bu çalışmada, birden fazla depotun (Giriş/Çıkış noktası) bulunduğu ve siparişlerin manuel olarak toplandığı 8 koridorlu tek bloklu bir depoda sipariş gruplama, depot atama ve rotalama problemini çözmek için “Aglomeratif Koridor Kümeleme (AKK)” adlı sezgisel bir algoritma önerilmiştir. Önerilen algoritmanın performansı iki farklı açıdan analiz edilmiştir. İlk olarak, önerilen sezgisel algoritmanın sipariş gruplama sürecine etkisini analiz etmek için, aynı depo yerleşim özelliklerinin kullanıldığı ancak sipariş gruplarının İlk Gelen İlk Hizmet Alır (İGİH) stratejisine göre oluşturulduğu yöntemle karşılaştırılmıştır. İkinci olarak, hem birden fazla depotun varlığının hem de önerilen sezgisel algoritmanın bütünleşik etkisini analiz etmek için, 8 koridorlu tek bloklu bir depoda sipariş gruplarını İGİH stratejisine göre oluşturan ancak en solda bir depotu bulunan yöntemle karşılaştırılmıştır. Analizler için 20, 40 ve 60 farklı müşteri siparişi içeren müşteri sipariş veri tabanları rastgele oluşturulmuş ve her bir grup için 30 deneme gerçekleştirilmiştir. Sonuçlar, toplam sipariş toplama mesafesini en aza indirmeyi amaçlayan önerilen algoritmanın üç sipariş grubunda ortalama %15 daha iyi performans gösterdiğini ortaya koymaktadır.

Anahtar Kelimeler

Depo yönetimi , Sipariş gruplama , Depot atama , Çoklu depot

Agglomerative Aisle Clustering Approach with Multiple Depots to Solve Order Batching, Depot Assignment, and Routing Problem

Öz

In this study, a heuristic algorithm named “Agglomerative Aisle Clustering (AAC)” is proposed to solve the order batching, depot assignment, and routing problem in a single-block warehouse with 8 aisles, where multiple depots are present and orders are picked manually. The performance of the proposed algorithm is analyzed from two different perspectives. Firstly, in order to analyze the effect of the proposed heuristic algorithm on the order batching process, it is compared with the method in which the same warehouse layout properties are used but the order batches are constructed according to the First-Come-First-Served (FCFS) strategy. Secondly, in order to analyze the integrated effect of both the presence of multiple depots and the proposed heuristic algorithm, it is compared with the method that forms the order batches according to the FCFS strategy in a single block with 8 aisles warehouse but with one left-most located depot. For the analyses, customer order databases containing 20, 40, and 60 different customer orders are randomly generated, and 30 experiments are conducted for each group. The results demonstrate that the proposed algorithm, aimed at minimizing the total order picking distance, performs 15% better on average across the three order groups.

Anahtar Kelimeler

Warehouse management , Order batching , Depot assignment , Multiple depots

Kaynakça

• Aboelfotoh, A. H. F. (2019). Optimizing the Multi-Objective Order Batching Problem for Warehouses with Cluster Picking (Master's Thesis). Ohio University, Ohio.
• Aboelfotoh, A., Singh, M., & Suer, G. (2019). Order Batching Optimization for Warehouses with Cluster-Picking. Procedia Manufacturing, 39, 1464–1473. Doi: https://doi.org/10.1016/j.promfg.2020.01.302
• Alipour, M., Mehrjedrdi, Y. Z., & Mostafaeipour, A. (2020). A rule-based heuristic algorithm for on-line order batching and scheduling in an order picking warehouse with multiple pickers. RAIRO - Operations Research, 54(1), Article 1. Doi: https://doi.org/10.1051/ro/2018069
• Almufti, S. M., Shaban, A. A., Ali, R. I., & Fuente, J. D. (2023). Overview of metaheuristic algorithms. Polaris Global Journal of Scholarly Research and Trends, 2(2), 10-32. Doi: https://doi.org/10.58429/pgjsrt.v2n2a144
• 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. Doi: https://doi.org/10.1016/j.ijpe.2018.10.001
• Binici, M., & Yenisey, M. M. (2023). Human Energy Expenditure in High-Level Order Picking. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 12(3), Article 3. Doi: https://doi.org/10.17798/bitlisfen.1336357
• Bottani, E., Casella, G., & Murino, T. (2021). A hybrid metaheuristic routing algorithm for low-level picker-to-part systems. Computers & Industrial Engineering, 160, 107540. Doi: https://doi.org/10.1016/j.cie.2021.107540
• Cano, J. A., Cortés, P., Campo, E. A., & Correa-Espinal, A. A. (2021). Multi-Objective Grouping Genetic Algorithm for the Joint Order Batching, Batch Assignment, and Sequencing Problem. International Journal of Management Science and Engineering Management, 0(0), 1–17. Doi: https://doi.org/10.1080/17509653.2021.1991852
• Chackelson, C., Errasti, A., Ciprés, D., & Lahoz, F. (2013). Evaluating order picking performance trade-offs by configuring main operating strategies in a retail distributor: A Design of Experiments approach. International Journal of Production Research, 51(20), Article 20. Doi: https://doi.org/10.1080/00207543.2013.796421
• Cheng, C.-Y., Chen, Y.-Y., Chen, T.-L., & Jung-Woon Yoo, J. (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. Doi: https://doi.org/10.1016/j.ijpe.2015.03.021
• 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), Article 2. Doi: https://doi.org/10.1016/j.ejor.2006.07.009
• Elevli, B., & Dinler, A. (2023). Multi-criteria approach for inventory classification and effective warehouse management. International Journal of Logistics Systems and Management, 45(1), 31–49. Doi: https://doi.org/10.1504/IJLSM.2023.130969
• Ene, S., & Öztürk, N. (2012). Storage location assignment and order picking optimization in the automotive industry. The International Journal of Advanced Manufacturing Technology, 60(5–8), Article 5–8. Doi:https://doi.org/10.1007/s00170-011-3593-y
• Gademann, N., & Velde, S. (2005). Order batching to minimize total travel time in a parallel-aisle warehouse. IIE Transactions, 37(1), 63–75. Doi: https://doi.org/10.1080/07408170590516917
• Gil-Borrás, S., Pardo, E. G., Alonso-Ayuso, A., & Duarte, A. (2021). A heuristic approach for the online order batching problem with multiple pickers. Computers & Industrial Engineering, 160, 107517. Doi: https://doi.org/10.1016/j.cie.2021.107517
• Gülyeşil, S., & Durmuşoğlu, Z. D. U. (2024). A new mathematical model and meta-heuristic algorithm for order batching, depot selection, and assignment problem with multiple depots and pickers. Computers & Industrial Engineering, 197, 110585. Doi: https://doi.org/10.1016/j.cie.2024.110585
• Günay, E. (2024). A two-stage stochastic model for picker allocation problem in warehouses considering the rest allowance and picker’s weight. International Journal of Industrial Engineering Computations, 15(3), 685–704. Doi: https://doi.org/10.5267/j.ijiec.2024.5.001
• Guo, X., Yu, Y., & De Koster, R. B. M. (2016). Impact of required storage space on storage policy performance in a unit-load warehouse. International Journal of Production Research, 54(8), 2405–2418. Doi: https://doi.org/10.1080/00207543.2015.1083624
• 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: https://doi.org/10.1007/s10696-021-09425-8
• Henn, S. (2012). Algorithms for on-line order batching in an order picking warehouse. Computers & Operations Research, 39(11), 2549–2563. Doi: https://doi.org/10.1016/j.cor.2011.12.019
• Henn, S. (2015). Order batching and sequencing for the minimization of the total tardiness in picker-to-part warehouses. Flexible Services and Manufacturing Journal, 27(1), 86–114. Doi: https://doi.org/10.1007/s10696-012-9164-1
• Henn, S., & Schmid, V. (2013). Metaheuristics for order batching and sequencing in manual order picking systems. Computers & Industrial Engineering, 66(2), 338–351. Doi: https://doi.org/10.1016/j.cie.2013.07.003
• Henn, S., & Wäscher, G. (2012). Tabu search heuristics for the order batching problem in manual order picking systems. European Journal of Operational Research, 222(3), Article 3. Doi: https://doi.org/10.1016/j.ejor.2012.05.049
• Hojaghani, L., Nematian, J., Shojaie, A. A., & Javadi, M. (2021). Metaheuristics for a new MINLP model with reduced response time for on-line order batching. Scientia Iranica, 28(5), 2789–2811. Doi: https://doi.org/10.24200/sci.2019.51452.2185
• Hsieh, L., & Tsai, L. (2006). The optimum design of a warehouse system on order picking efficiency. The International Journal of Advanced Manufacturing Technology, 28(5–6), Article 5–6. Doi: https://doi.org/10.1007/s00170-004-2404-0
• Hsieh, L.-F., & Huang, Y.-C. (2011). New batch construction heuristics to optimise the performance of order picking systems. International Journal of Production Economics, 131(2), 618–630. Doi: https://doi.org/10.1016/j.ijpe.2011.02.006
• Hsu, C.-M., Chen, K.-Y., & Chen, M.-C. (2005). Batching orders in warehouses by minimizing travel distance with genetic algorithms. Computers in Industry, 56(2), Article 2. Doi: https://doi.org/10.1016/j.compind.2004.06.001
• 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. Doi: https://doi.org/10.1016/j.cie.2020.106645
• 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. Doi: https://doi.org/10.1007/s10696-011-9101-8
• 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. Doi: https://doi.org/10.1016/j.cie.2016.03.009
• Muter, İ., & Öncan, T. (2015). An exact solution approach for the order batching problem. IIE Transactions, 47(7), Article 7. Doi: https://doi.org/10.1080/0740817X.2014.991478
• Öncan, T. (2015). MILP formulations and an Iterated Local Search Algorithm with Tabu Thresholding for the Order Batching Problem. European Journal of Operational Research, 243(1), Article 1. Doi: https://doi.org/10.1016/j.ejor.2014.11.025
• Petersen, C. G., & Aase, G. (2004). A comparison of picking, storage, and routing policies in manual order picking. International Journal of Production Economics, 92(1), Article 1. Doi: https://doi.org/10.1016/j.ijpe.2003.09.006
• Petersen, C. G., Aase, G. R., & Heiser, D. R. (2004). Improving order‐picking performance through the implementation of class‐based storage. International Journal of Physical Distribution & Logistics Management, 34(7), 534–544. Doi: https://doi.org/10.1108/09600030410552230
• 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: https://doi.org/10.1016/j.rico.2023.100281
• Roodbergen, K. J., & de Koster, R. (2001). Routing order pickers in a warehouse with a middle aisle. European Journal of Operational Research, 133(1), Article 1. Doi: https://doi.org/10.1016/S0377-2217(00)00177-6
• Saylam, S., Çelik, M., & Süral, H. (2023). The min–max order picking problem in synchronised dynamic zone-picking systems. International Journal of Production Research, 61(7), 2086–2104. Doi: https://doi.org/10.1080/00207543.2022.2058433
• Saylam, S., Çelik, M., & Süral, H. (2024). Arc routing based compact formulations for picker routing in single and two block parallel aisle warehouses. European Journal of Operational Research, 313(1), 225–240. Doi: https://doi.org/10.1016/j.ejor.2023.08.018
• 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. Doi: https://doi.org/10.1016/j.ejor.2017.04.038
• Shqair, M., Altarazi, S., & Al-Shihabi, S. (2014). A statistical study employing agent-based modeling to estimate the effects of different warehouse parameters on the distance traveled in warehouses. Simulation Modelling Practice and Theory, 49, 122–135. Doi: https://doi.org/10.1016/j.simpat.2014.08.002
• Theys, C., Bräysy, O., Dullaert, W., & Raa, B. (2010). Using a TSP heuristic for routing order pickers in warehouses. European Journal of Operational Research, 200(3), 755–763. Doi: https://doi.org/10.1016/j.ejor.2009.01.036
• Tran-Vo, T. H., Nguyen, T. M., & Hong, S. (2022). Effects of Multiple Depots on Total Travel Distance in Parallel-Aisle Manual Order Picking Systems. In D. Y. Kim, G. von Cieminski, & D. Romero (Eds.), Advances in Production Management Systems. Smart Manufacturing and Logistics Systems: Turning Ideas into Action (pp. 310–318). Springer Nature Switzerland. Doi: https://doi.org/10.1007/978-3-031-16407-1_37
• Tutam, M., & De Koster, R. (2024). To walk or not to walk? Designing intelligent order picking warehouses with collaborative robots. Transportation Research Part E: Logistics and Transportation Review, 190, 103696. Doi: https://doi.org/10.1016/j.tre.2024.103696
• 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. Doi: https://doi.org/10.1016/j.ejor.2017.03.069
• van der Gaast, J. P., Jargalsaikhan, B., & Roodbergen, K. (2018). Dynamic Batching for Order Picking in Warehouses. Progress in Material Handling Research. Retrieved from https://digitalcommons.georgiasouthern.edu/pmhr_2018/20
• 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. Doi: https://doi.org/10.1016/j.ejor.2019.03.012
• Wang, Y., Wang, Z., & Mi, S. (2017). An order batching clustering algorithm of fixed maximum order number based on order picking system. 2017 4th International Conference on Industrial Economics System and Industrial Security Engineering (IEIS), 1–6. Doi: https://doi.org/10.1109/IEIS.2017.8078640
• Weidinger, F., Boysen, N., & Schneider, M. (2019). Picker routing in the mixed-shelves warehouses of e-commerce retailers. European Journal of Operational Research, 274(2), Article 2. Doi: https://doi.org/10.1016/j.ejor.2018.10.021
• Won, J., & Olafsson, S. (2005). Joint order batching and order picking in warehouse operations. International Journal of Production Research, 43(7), 1427–1442. Doi: https://doi.org/10.1080/00207540410001733896
• Xie, L., Li, H., & Luttmann, L. (2023). Formulating and solving integrated order batching and routing in multi-depot AGV-assisted mixed-shelves warehouses. European Journal of Operational Research, 307(2), 713–730. Doi: https://doi.org/10.1016/j.ejor.2022.08.047
• Yu, Y., Koster, R. B. M. de, & Guo, X. (2015). Class-Based Storage with a Finite Number of Items: Using More Classes is not Always Better. Production and Operations Management, 24(8), Article 8. Doi: https://doi.org/10.1111/poms.12334