Sipariş toplama sürecinde iş yükü dengesinin sezgisel atama yaklaşımlarıyla analizi

Abstract

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.

Keywords

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

Ethical Statement

Bu çalışma için etik kurul onayı alınmasına gerek bulunmamaktadır.

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

Abstract

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.

Keywords

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

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