Aralıklı Taleplerde Envanter Kontrolü için Genetik Algoritmaya Dayalı Bir Model

Year 2021, , 696 - 701, 31.12.2021

Ferhat Yuna , Burak Erkayman

https://doi.org/10.31590/ejosat.1039251

Abstract

Talep tahmini, aralıklı talepler için zor bir çalışma alanıdır. Yedek parça talep yapıları da aralıklı bir talep yapısına sahiptir. Dolayısıyla bu alanda faaliyet gösteren firmalar için bu durum çeşitli sorunlara (elde tutma maliyeti veya kayıp satış maliyeti) neden olmaktadır. Aralıklı talepleri tahmin etmek doğal olarak zordur. Düzgün bir yapıya sahip talepler, işletmeler için daha uygun bir çalışma ortamı sağlar. İlgili talep ne kadar doğru tahmin edilirse, talep tahminine dayalı çalışmalar da o kadar sorunsuz yürütülür. Bu çalışmada, aralıklı talep yapısı ile rastgele oluşturulmuş bir talep serisi incelenmiştir. Aralıklı talebin tahmin zorluğu, Matlab'da yapılan bir tahminle gösterilmiştir. Bu zorluğun önüne geçebilmek için stok seviyeleri belirlenerek maliyetler minimize edilmeye çalışılmıştır. Aralıklı talepleri kullanarak stok seviyelerini belirleyen ve maliyetleri hesaplayarak ortalama karı hesaplayan bir envanter modeli önerilmiştir. İlgili model Matlab'da Genetik Algoritma ile çözülmüş ve sonuçlar kaydedilmiştir.

Keywords

Genetik Algoritma, Aralıklı Talep, Envanter Kontrol, Optimizasyon

A Genetic Algorithm-Based Model for Inventory Control in Intermittent Demands

Abstract

Demand forecasting is a difficult field of study for intermittent demands. Spare parts demand structures also have an intermittent demand structure. Therefore, for companies operating in this field, this situation causes various problems (holding cost or cost of lost sale). Intermittent demands are inherently difficult to predict. Demands with a smooth structure provide a more suitable working environment for businesses. Because the more accurately the relevant demand is forecasted, the more smoothly the works that depend on demand forecasting are carried on. In this study, a randomly generated demand series with intermittent demand structure is examined. The estimation difficulty of intermittent demand is illustrated by an estimation made in Matlab. In order to avoid this difficulty, the costs were tried to be minimized by determining the inventory levels. An inventory model is proposed that determines stock levels using intermittent demands and calculates average profit by calculating costs. The related model was solved with Genetic Algorithm in Matlab and the results were recorded.

Keywords

Genetic Algorithm, Intermittent Demand, Inventory Control, Optimization

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