Marketing

City logistics

Algorithm

Engineering

Transport engineering

Pure mathematics

Field (mathematics)

Computer science

Risk analysis (engineering)

Business

Reinforcement learning

Process management

State of art

Supply chain management

Mathematics

Logistics management

Supply chain

Data science

Artificial intelligence

Knowledge management

Engineering management

State (computer science)

Transportation Research Part E Logistics and Transportation Review

With advances in technologies, data science techniques, and computing equipment, there has been rapidly increasing interest in the applications of reinforcement learning (RL) to address the challenges resulting from the evolving business and organisational operations in logistics and supply chain management (SCM). This paper aims to provide a comprehensive review of the development and applications of RL techniques in the field of logistics and SCM. We first provide an introduction to RL methodologies, followed by a classification of previous research studies by application. The state-of-the-art research is reviewed and the current challenges are discussed. It is found that Q-learning (QL) is the most popular RL approach adopted by these studies and the research on RL for urban logistics is growing in recent years due to the prevalence of E-commerce and last mile delivery. Finally, some potential directions are presented for future research.

Reinforcement learning for logistics and supply chain management: Methodologies, state of the art, and future opportunities