Sipariş toplama sürecinde iş yükü dengesinin sezgisel atama yaklaşımlarıyla analizi

Öz

Sipariş toplama süreçlerinde iş yükünün çalışanlar arasında dengeli biçimde dağıtılması, depo operasyonlarının etkinliği ve sürdürülebilirliği açısından kritik öneme sahiptir. Özellikle siparişlerin işlem süresi ve zorluk düzeyleri bakımından heterojen olduğu sistemlerde, yalnızca sipariş sayısına dayalı atama yaklaşımları iş yükü dengesini sağlamada yetersiz kalabilmektedir. Bu çalışma, sipariş toplama sürecinde iş yükü dengesinin sezgisel atama yaklaşımları kullanılarak nasıl iyileştirilebileceğini incelemeyi amaçlamaktadır. Çalışmada, toplam 425 birimlik iş yüküne sahip 100 siparişin 10 çalışana dağıtıldığı bir depo senaryosu ele alınmıştır. Tüm çalışanların eşit kapasiteye sahip olduğu varsayımı altında dört farklı atama yaklaşımı karşılaştırmalı olarak değerlendirilmiştir: eşit sayıda sipariş atamaya dayalı referans senaryo (EQL), ortalama temelli hedef iş yükü yaklaşımıyla uygulanan Açgözlü (Greedy) algoritma, hedefe duyarlı sezgisel bir yöntem olan Best-Fit-to-H yaklaşımı ve hedef iş yükü bilgisi içermeyen Round-Robin yöntemi. Yöntemlerin performansı; personel bazlı toplam iş yükleri, hedef iş yükünden sapmalar, standart sapma ve ortalama mutlak sapma (OMS) ölçütleri üzerinden analiz edilmiştir. Elde edilen bulgular, hedef iş yükü bilgisini doğrudan veya dolaylı olarak kullanan sezgisel yaklaşımların, hedef bilgisi içermeyen yöntemlere kıyasla iş yükü dengesini anlamlı ölçüde iyileştirdiğini göstermektedir. Özellikle Açgözlü algoritmasının, düşük hesaplama maliyetiyle hedef iş yükü etrafında oldukça dengeli bir dağılım sağladığı görülmüştür. Çalışma sonuçları, sipariş toplama süreçlerinde hedefe dayalı sezgisel atama yaklaşımlarının pratik ve etkili çözümler sunduğunu ortaya koymaktadır.

Anahtar Kelimeler

• Sipariş toplama
• İş yükü dengesi
• Sezgisel atama
• Açgözlü algoritma
• Depo operasyonları
• Hedef iş yükü

Etik Beyan

Bu çalışma için etik kurul onayı alınmasına gerek bulunmamaktadır.

Analysis of workload balance in order picking processes using heuristic assignment approaches

Öz

Balancing workload among employees in order picking processes is critical for the efficiency and sustainability of warehouse operations. In systems where orders are heterogeneous in terms of processing time and difficulty, assignment approaches based solely on an equal number of orders often fail to ensure a balanced workload distribution. This study aims to examine how workload balance in order picking operations can be improved using heuristic assignment approaches. A warehouse scenario in which 100 orders with a total workload of 425 units are assigned to 10 workers is considered. Under the assumption that all workers have equal capacity, four different assignment approaches are comparatively evaluated: an equal-order assignment reference scenario (EQL), a Greedy algorithm applied based on an average target workload, a target-sensitive heuristic approach known as Best-Fit-to-H, and a Round-Robin method that does not incorporate target workload information. The performance of these methods is assessed using worker-level total workloads, deviations from the target workload, standard deviation, and mean absolute deviation (MAD). The results indicate that heuristic approaches that directly or indirectly utilize target workload information significantly improve workload balance compared to methods that rely solely on order count or cyclic assignment. In particular, the Greedy algorithm achieves a highly balanced workload distribution around the target value with low computational effort. Overall, the findings demonstrate that target-based heuristic assignment approaches offer practical and effective solutions for improving workload balance in order picking processes.

Anahtar Kelimeler

• Order picking
• Workload balancing
• Heuristic assignment
• Greedy algorithm
• Warehouse operations
• Target workload

Kaynakça

1. Kudelska, I. ve Niedbał, R., Technological and organizational innovation in warehousing process-Research over workload of staff and efficiency of picking stations, E&M Economics and Management, 23(3), 67-81, (2020).
2. Lee, J. A., Chang, Y. S. ve Karwowski, W., Assessment of working postures and physical loading in advanced order picking tasks: A case study of human interaction with automated warehouse goods-to-picker systems, Work, 67(4), 855-866, (2020).
3. Bölük, U., İnsan kaynakları yönetiminde örgütsel adaletin verimlilik ve iş tatmini üzerine etkisi: Bir kamu kurumu örneği, Yüksek Lisans Tezi, İstanbul Gelişim Üniversitesi Sosyal Bilimler Enstitüsü, İstanbul, (2017).
4. Urzúa, M., Mendoza, A. ve González, A. O., Evaluating the impact of order picking strategies on the order fulfilment time: A simulation study, Acta Logistica International Scientific Journal of Logistics, 6(4), 103-114, (2019).
5. Vanheusden, S., Van Gils, T., Caris, A., Ramaekers, K. ve Braekers, K., Operational workload balancing in manual order picking, Computers & Industrial Engineering, 141, 106269, (2020).
6. Casella, G., Volpi, A., Montanari, R., Tebaldi, L. ve Bottani, E., Trends in order picking: A 2007–2022 review of the literature, Production & Manufacturing Research, 11(1), 2191115, (2023).
7. Vanheusden, S., Van Gils, T., Braekers, K., Ramaekers, K. ve Caris, A., Analysing the effectiveness of workload balancing measures in order picking operations, International Journal of Production Research, 60(7), 2126-2150, (2022).
8. Hendry, L., Huang, Y. ve Stevenson, M., Workload control: Successful implementation taking a contingency-based view of production planning and control, International Journal of Operations & Production Management, 33(1), 69-103, (2013).

9. Stevenson, M., Huang, Y., Hendry, L. C. ve Soepenberg, E., The theory and practice of workload control: A research agenda and implementation strategy, International Journal of Production Economics, 131(2), 689-700, (2011).
10. Ghandehari, M. ve Fattahian, S., An Assignment-Scheduling Model to Improve Employee Workload Using Job Rotation, Research in Production and Operations Management, 19-40, (2025).
11. Buchbinder, N. ve Naor, J., Fair online load balancing, Proceedings of the Eighteenth Annual ACM Symposium on Parallelism in Algorithms and Architectures, 291-298, (2006).
12. Buchbinder, N. ve Naor, J., Fair online load balancing, Journal of Scheduling, 16(1), 117-127, (2013).
13. Farkas, A., Koltai, T. ve Stecke, K. E., Workload balancing using the concept of operation types, University of Michigan Business School Working Paper, 99-002, (1999).
14. Shim, D. H. ve Lee, Y. H., The Workload Assignment Problem in consideration of the Worker Pairing and the Workload Balancing, IE Interfaces, 22(3), 263-277, (2009).
15. Pinhão, J. D. M. O. B., Ignacio, A. A. V. ve Coelho, O., An integer programming mathematical model with line balancing and scheduling for standard work optimization: A realistic application to aircraft engines assembly lines, Computers & Industrial Engineering, 173, 108652, (2022).
16. Wang, F., Wang, Y. ve Chang, D., Joint Optimization of Order Allocation and Rack Selection in the “Parts-to-Picker” Picking System Considering Multiple Stations Workload Balance, Systems, 11(4), 179, (2023).
17. Saylam, S., Çelik, M. ve Süral, H., The min–max order picking problem in synchronised dynamic zone-picking systems, International Journal of Production Research, 61(7), 2086-2104, (2023).
18. Krishnamurthy, D., Rolia, J. ve Xu, M., WAM-The weighted average method for predicting the performance of systems with bursts of customer sessions, IEEE Transactions on Software Engineering, 37(5), 718-735, (2011).
19. Jyothi, P. D. ve Vasundra, S., The Weighted Average Method for Predicting the Performance of Systems, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 1(7), (2012).
20. Biswas, S., Belamkar, P., Sarma, D., Tirkolaee, E. B. ve Bera, U. K., A multi-objective optimization approach for resource allocation and transportation planning in institutional quarantine centres, Annals of Operations Research, 346(2), 781-825, (2025).
21. Orçanlı, K., Stratejik lojistikte karar verme yöntemleri, Stratejik Lojistik Yönetimi, 113-152, (2020).
22. Pinedo, M. L., Design and implementation of scheduling systems: More advanced concepts, In: Scheduling: Theory, Algorithms, and Systems, Springer International Publishing, Cham, 485-508, (2016).
23. Jungnickel, D., The greedy algorithm, In: Graphs, Networks and Algorithms, Springer Berlin Heidelberg, Berlin, Heidelberg, 135-161, (2013).
24. Vince, A., A framework for the greedy algorithm, Discrete Applied Mathematics, 121(1-3), 247-260, (2002).
25. García, A., Greedy algorithms: A review and open problems, Journal of Inequalities and Applications, 2025(1), 11, (2025).
26. Sun, H., Elghazi, R., Gainaru, A., Aupy, G. ve Raghavan, P., Scheduling parallel tasks under multiple resources: List scheduling vs. pack scheduling, In: 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS), IEEE, 194-203, (2018).
27. Ouazene, Y., Nguyen, N. Q. ve Yalaoui, F., Workload balancing on identical parallel machines: Theoretical and computational analysis, Applied Sciences, 11(8), 3677, (2021).
28. Aguilar, J. ve Gelenbe, E., Task assignment and transaction clustering heuristics for distributed systems, Information Sciences, 97(1-2), 199-219, (1997).
29. Lo, V. M., Heuristic algorithms for task assignment in distributed systems, IEEE Transactions on Computers, 37(11), 1384-1397, (2002).
30. Page, A. J., Keane, T. M. ve Naughton, T. J., Multi-heuristic dynamic task allocation using genetic algorithms in a heterogeneous distributed system, Journal of Parallel and Distributed Computing, 70(7), 758-766, (2010).
31. Rasmussen, R. V. ve Trick, M. A., Round robin scheduling–a survey, European Journal of Operational Research, 188(3), 617-636, (2008).
32. Choudhary, S., Suresh, K. ve Sham, M., Enhanced task scheduling mechanism in logistics based on improvised round robin technique, In: 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon), IEEE, 1-7, (2022).