Depo Operasyonlarının Planlanması İçin Genetik Algoritma Esaslı Modeller

Year 2013, Volume: 5 Issue: 3, 141 - 153, 01.09.2013

Yusuf Şahin Osman Kulak

Abstract

Nowadays one of the most important aims of the companies is to manage warehouse operations effectively. During the management of warehouse operations two types of problems are experienced densely. First one is to define order batches and second one is to determine a suitable vehicle route for batched orders. In order to prepare effective and economic distribution plans, these problems should be solved by companies simultaneously. In this study, novel cluster-based genetic algorithm approaches namely Genetic AlgorithmNearest Neighbor (GANN) and Genetic Algorithm-Saving (GAS) are proposed to solve order batching and vehicle routing of relevant batch for cross aisle warehouse systems especially. To evaluate the performances of the proposed methods, various problem instances including the number of order, weight and picking coordinates are generated randomly and tested. 1.

Keywords

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Depo Operasyonlarının Planlanması İçin Genetik Algoritma Esaslı Modeller

Abstract

Günümüzde depo operasyonlarının etkin olarak yönetilmesi firmalarının en önemli hedeflerinden birisidir. Operasyonların yönetilmesi sırasında yoğun olarak iki tip problemle karşılaşılmaktadır. Bunlardan birincisi sipariş hazırlıklarına yönelik siparişlerin gruplandırılması, ikincisi ise gruplanan siparişlere ait toplama aracı rotasının belirlenmesidir. Firmalarının etkin ve ekonomik sipariş toplama planlarını oluşturabilmeleri içi n bu problemlerin eş zamanlı olarak çözmeleri gerekmektedir. Bu çalışmada, özellikle çapraz geçitli depo sistemleri için sipariş gruplarını ve ilgili gruplara ait araç rotalarını birlikte çözen kümeleme esaslı Genetik AlgoritmaEn Yakın Komşu ve Genetik Algoritma - Kazanç isimli genetik algoritma esaslı yeni yöntemler önerilmektedir. Yöntemlerin etkinliğini belirlemek için farklı sipariş sayısı, ağırlık ve toplama koordinatlarını içeren deney problemleri rastsal olarak oluşturulmuş ve test edilmiştir.

Keywords

Sipariş Toplama, Sipariş Gruplama, Genetik Algoritma

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