Reinforcement learning approaches for specifying ordering policies of perishable inventory systems

作者：

Highlights：

• Developed RL policies perform better than the other algorithms.

• RL learn better when the age of the products is used in state representation.

• Age and demand variance are important for perishable inventory management.

• The value of the age becomes critical when the lifetime of the product decreases.

摘要

•Developed RL policies perform better than the other algorithms.•RL learn better when the age of the products is used in state representation.•Age and demand variance are important for perishable inventory management.•The value of the age becomes critical when the lifetime of the product decreases.

论文关键词：Reinforcement learning,Inventory management system,Simulation-based optimization,Ordering management,Perishable item

论文评审过程：Received 23 May 2017, Revised 23 August 2017, Accepted 24 August 2017, Available online 30 August 2017, Version of Record 7 September 2017.

论文官网地址：https://doi.org/10.1016/j.eswa.2017.08.046