KAPASİTE KISITLI STOKASTİK STOK PROBLEMİ İÇİN BENZETİMSEL DİNAMİK PROGRAMLAMA SEZGİSELİ

Year 2016, Volume: 2 Issue: 1, 43 - 64, 17.02.2016

Mustafa Çimen , Sedat Belbağ Mehmet Soysal

Abstract

Rekabetçi bir ortamda faaliyet gösteren işletmeler için belirsizlik altında stok yönetimi oldukça önemlidir. Bu çalışma kapasite kısıtlı stokastik stok problemine Benzetimsel Dinamik Programlama (Approximate Dynamic Programming‐ADP) tabanlı bir sezgisel yöntem önermektedir. Önerilen sezgisel yöntem, Geçici Farklarla Öğrenme (Temporal Difference Learning‐TD) yöntemini kullanmaktadır. Söz konusu problem için sezgisel yöntemden elde edilen çözümlerin performansı, Dinamik Programlama algoritmasından elde edilen optimal sonuçlar ilekarşılaştırılarak değerlendirilmiştir. Sayısal örnekler sonucunda ADP’nin kısa süre içerisinde ümit vadeden sonuçlar verdiği gözlenmiştir.

Keywords

Stokastik Stok Problemi, Kapasite Kısıtı, Dinamik Programlama, Sezgisel, Benzetimsel Dinamik Programlama

AN APPROXIMATE DYNAMIC PROGRAMMING HEURISTIC FOR THE CAPACITATED STOCHASTIC INVENTORY PROBLEM

Abstract

Inventory management under uncertainty is crucial for enterprises operating in competitive environment. This study presents a heuristic based on Approximate Dynamic Programming for the capacitated stochastic inventory problem. The proposed heuristic employs Temporal Difference Learning method. The performance of the heuristic solutions for the aforementioned problem has been assessed relative to the optimal solutions obtained through Dynamic Programming algorithm. Numerical experiments show that Approximate Dynamic Programming provides promising solutions within relatively short computational times.

Keywords

Stochastic Inventory Problem, Capacity Constraint, Dynamic Programming, Heuristic, Approximate Dynamic Programming

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