Applying machine learning to the dynamic selection of replenishment policies in fast-changing supply chain environments.
Paolo PrioreBorja PonteRafael RosilloDavid de la FuentePublished in: Int. J. Prod. Res. (2019)
Keyphrases
- supply chain
- machine learning
- bullwhip effect
- management policies
- lead time
- inventory management
- supply chain management
- operating costs
- replenishment policy
- service level
- quantity discount
- inventory policy
- cost savings
- data mining
- revenue sharing
- decision making
- inventory control
- stackelberg game
- planning horizon
- optimal policy
- inventory holding
- supplier selection
- customer demand
- lost sales
- single item
- markov decision processes