Neural combinatorial optimization with reinforcement learning in industrial engineering: a survey

Abstract

In recent trends, machine learning is widely used to support decision-making in various domains and industrial operations. Because of the increasing complexity of modern industries, industrial engineering aims not only to increase cost-effectiveness and productivity but also to consider sustainability, resilience, and human centricity, resulting in many-objective, constrained, and stochastic operations research. Based on the above stringent requirements, combinatorial optimization (CO) problems are thus developed to support the complicated decision-making process in operations research. Due to the computational complexity of exact algorithms and the uncertain solution quality of heuristic methods, there is a growing trend to leverage the power of machine learning in solving CO problems, known as neural combinatorial optimization (NCO), where reinforcement learning (RL) is the core to achieve the sequential decision support. This survey study provides a comprehensive investigation of the theories and recent advancements in applying RL to solve hard CO problems, such as vehicle routing, bin packing, assignment, scheduling, and planning problems, and, in addition, summarizes the applications of neural combinatorial optimization with reinforcement learning (NCO-RL). The detailed review found that although the research domain of NCO-RL is still under-explored, its research potential has been proven to address environmental sustainability, adaptability, and human factors. Last but not least, the technical challenges and opportunities of the NCO-RL to embrace the industry 5.0 paradigm are discussed.