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

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 sustainabili...

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Vydáno v:The Artificial intelligence review Ročník 58; číslo 5; s. 130
Hlavní autoři: Chung, K. T., Lee, C. K. M., Tsang, Y. P.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Dordrecht Springer Netherlands 14.02.2025
Springer Nature B.V
Témata:
Algorithms
Artificial Intelligence
Combinatorial analysis
Complexity
Computer Science
Cost analysis
Cost effectiveness
Decision making
Engineering
Heuristic
Heuristic methods
Human factors
Industrial engineering
Machine learning
Operations research
Optimization
Polls & surveys
Productivity
Reinforcement
Resilience
Sustainability
Sustainable development
Vehicle routing
Industrial engineering
Neural combinatorial optimization
Operations research
Review
Reinforcement learning
ISSN:1573-7462, 0269-2821, 1573-7462
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Shrnutí: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.
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ISSN:1573-7462
0269-2821
1573-7462
DOI:10.1007/s10462-024-11045-1