Makine Çizelgeleme Problemlerinin Çözümünde Pekiştirmeli Öğrenme Etkisinin Analizi

Year 2024, , 116 - 140, 30.08.2024

Bünyamin Sarıcan , Orhan Engin

https://doi.org/10.46740/alku.1390397

Abstract

Pekiştirmeli öğrenme, günümüz dünyasında birçok gerçek hayat problemine çözüm bulmada aktif bir şekilde kullanılmakta ve endüstri içerisinde de umut verici yöntemler arasında gösterilmektedir. Bu çalışmada, makine öğrenmesinin bir alt dalı olan pekiştirmeli öğrenmenin iş çizelgeleme problemlerinin çözümündeki etkisi araştırılmıştır. Bu kapsamda, öncelikle pekiştirmeli öğrenmede durum tanımı, eylem seçimi ve öğrenme algoritmaları açıklanmıştır. Ardından, iş çizelgeleme probleminin sınıflandırmasına yer verilmiştir. Literatürde yer alan iş çizelgelemede, pekiştirmeli öğrenme yönteminin kullanıldığı, son yirmi yılda yayımlanan, 50 makale çalışmasına yer verilmiştir. Literatürde yer alan çalışmaların çizelgeleme problemlerinin çözümü üzerinde gösterdiği etki değerlendirilmiştir. Son bölümde pekiştirmeli öğrenmenin diğer çözüm yöntemlerine kıyasla güçlü ve zayıf yönlerine yer verilmiş ayrıca gelecekte yapılacak araştırmalara yönelik değerlendirmelerde bulunulmuştur.

Keywords

Çizelgeleme Optimizasyonu, Makine Öğrenmesi, İş Çizelgeleme, Pekiştirmeli Öğrenme

Analysis of Reinforcement Learning Effect in Solving Scheduling Problems

Abstract

Reinforcement learning is actively used to find solutions to many real life problems in today's world and is shown among the promising methods in the industry. This study investigated the effect of reinforcement learning, which is a sub-branch of machine learning, in solving job scheduling problems. In this context, first of all, situation definition, action selection and learning algorithms in reinforcement learning are explained. Then, the classification of the job scheduling problem is given. In the literature, 50 articles published in the last twenty years, in which the reinforcement learning method is used in job scheduling, are included. The effects of the studies in the literature on the solution of scheduling problems were evaluated. In the last section, the strengths and weaknesses of reinforcement learning compared to other solution methods are included and evaluations for future research are made.
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Keywords

Scheduling Optimization, Machine Learning, Job Scheduling, Reinforcement Learning

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