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Depolama sistemlerinde sipariş toplama işlemlerinin genetik algoritmalarla optimizasyonu

OPTIMIZATION OF ORDER PICKING OPERATIONS WITH GENETIC ALGORITHMS IN WAREHOUSE SYSTEMS

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Abstract (2. Language): 
Order picking means the retrieval of products from storage to meet customers' demand is the most labor intensive operation in warehouse and distribution center. The order picking problem aims to determine batches of orders and find routes of the order pickers for these batches in such a way that minimize the total orderpickers' travel distance in warehouse, hence the order picking costs are reduced. In this study, after a brief overview of warehouse operations, an overview of order-picking methods and their potentials in solution of this problem especially in parallel back to back rack systems include cross aisles, based mainly on reducing travelling distances. Proposed genetic algorithm (GA) with group based coding using different parameters is systematically compared parallel Clarke-Wright (CW) traditional heuristic route method. For all data sets using this study, GA results are better than the results of parallel CW algorithm.
Abstract (Original Language): 
Depolama ve dağıtım merkezlerinde iş gücünün en yoğun olarak kullanıldığı operasyonlar olan sipariş toplama işlemleri; müşterilerin talepleri doğrultusunda siparişlerin depo içerisinde bulundukları noktalardan alınmalarını ifade etmektedir. Sipariş toplama problemi; operasyonel maliyeti düşürmek için, toplayıcıların kat ettiği mesafenin minimize edilmesini sağlamak amacıyla, siparişlerin uygun şekilde gruplanmasını ve sipariş toplayıcıların rotalarının tespit edilmesini ifade etmektedir. Bu çalışmada depolama operasyonlarının açıklanmasını takiben; sipariş toplayıcı seyahat mesafesinin azaltılması amacıyla, sipariş toplama probleminin çözümünde kullanılan teknikler irdelenmiştir. Özellikle çapraz geçit içeren, sırt sırta raf sistemlerine sahip depolar dikkate alınmıştır. Çalışma kapsamında önerilen gruba dayalı kodlamalı genetik algoritma (GA) yöntemi farklı parametreler kullanılarak, geleneksel bir gruplama ve rotalama metodu olan paralel Clarke-Wright (CW) sezgiseli ile karşılaştırılmıştır. Uygulamada kullanılan tüm veri setleri için GA yöntemi, paralel CW algoritmasına göre daha iyi sonuçlar ortaya koymuştur.
118-144

REFERENCES

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