Reinforcement learning based intelligent optimisation for bin packing problems: A review

The convergence of Reinforcement Learning (RL) and Bin Packing Problems (BPP) is a critical field of study that has profound ramifications in logistics, manufacturing, computer, and retail industries. This paper thoroughly examines the progression from simple rule-based tactics to advanced Deep Reinforcement Learning (DRL) techniques in solving BPPs. By conducting a thorough review of 231 papers conducted between 2019 and 2024, we address and provide answers to important research inquiries, such as “To what extent has academic research explored the use of RL for BPP during this time frame?” and “Which specific areas of application and methodologies have been predominantly used?” Our examination highlights a significant and rapid growth in research activity in this field. The study reveals a clear inclination towards DRL compared to traditional RL techniques, especially in complex, multi-dimensional BPP situations. It also identifies a growing interest in hybrid models and transfer learning methods as potential solutions to the challenges of scalability, computational requirements, and the exploration-exploitation trade-off. This study shows that some DRL models are highly effective in complex BPP scenarios. It suggests that future research should focus on scalability, operational efficiency, and the practical implementation of theoretical achievements in industry. This study aims to promote multidisciplinary discourse and collaboration in optimisation and artificial intelligence by comprehensively analysing current achievements and identifying the remaining problems.