Pekiştirmeli Öğrenme ile Sipariş Yığınlama ve Sıralama Probleminin Çözülmesi

Öz

Bu araştırmanın amacı bir DRL çözümünün OBSP problemi için uygun bir çözüm olup olmayacağını belirlemek ve geleneksel yöntemlerle karşılaştırmaktır. Bu amaçla karmaşık ve gerçekçi bir warehouse ortamında PPO algoritması ile eğitilen modeller test edilmiş, geç siparişlerin sayısını azaltacak bir strateji geliştirilip geliştirilmediği ölçülmeye çalışılmıştır. Bir heuristic metod da aynı ortam ve veriler üzerinde uygulanarak sonuçlar karşılaştırılmıştır. Sonuçlar, heuristic yöntemi PPO algoritmasıyla birleştiren DRL yaklaşımının, tüm test edilen senaryolarda geç kalan sipariş yüzdesini en aza indirmede heuristic yöntemlere göre daha iyi bir performansa sahip olduğunu gösterdi.

Anahtar Kelimeler

• Pekiştirmeli Öğrenme
• Sipariş Gruplama ve Sıralama Problemi
• Yakın Politika Optimizasyonu
• Depo Optimizasyonu

Solving an Order Batching and Sequencing Problem with Reinforcement Learning

Öz

The purpose of this research is to determine whether a DRL solution would be a suitable solution for the OBSP problem and to compare it with traditional methods. For this purpose, models trained utilizing the PPO algorithm were tested in a complex and realistic warehouse environment, and an attempt was made to measure whether a strategy was developed to decrease the number of orders being late. A heuristic method was also applied and the results were compared on the same environment and data. The results showed that DRL approach that combines heuristics with the PPO algorithm outperforms the heuristics in minimizing the tardy order percentage in all tested scenarios.

Anahtar Kelimeler

• Reinforcement Learning
• Order Batching and Sequencing
• Proximal Policy Optimization
• Warehouse Optimization

Kaynakça

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