A cooperative hierarchical deep reinforcement learning based multi-agent method for distributed job shop scheduling problem with random job arrivals

Huang, Jiang-Ping; Gao, Liang; Li, Xin-Yu; Zhang, Chun-Jiang

doi:10.1016/j.cie.2023.109650

Cited by 7 publications

(2 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this evaluation, the method demonstrated its superiority to the established dispatching rules and pre-existing DRL techniques in terms of the mean and variance of the total tardiness obtained. The importance and the flexibility of the DRL-based multi-agent method are also highlighted by Huang J, et al [56]. They solve a distributed JSSP (DJSSP) for an automotive engine manufacturing company.…”

Section: Reinforcement Learningmentioning

confidence: 99%

“…The authors tested the solution in a real industrial case, and the algorithm turned out to provide a lower disassembly time than other algorithms, such as a GA, an improved discrete bee algorithm, and a dueling DQN. RL-Q-learning x [51] RL-AC algorithm x [52] RL-Q-learning x [53] RL-Q-learning + CTPNs x [54] RL-Q-learning x [55] MARL-Deep RL x [56] MARL-Deep RL x [57] MARL-SARSA x [58] RL-DQN x…”

Section: Reinforcement Learningmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence to Solve Production Scheduling Problems in Real Industrial Settings: Systematic Literature Review

Del Gallo,

Mazzuto,

Ciarapica

et al. 2023

Electronics

View full text Add to dashboard Cite

This literature review examines the increasing use of artificial intelligence (AI) in manufacturing systems, in line with the principles of Industry 4.0 and the growth of smart factories. AI is essential for managing the complexities in modern manufacturing, including machine failures, variable orders, and unpredictable work arrivals. This study, conducted using Scopus and Web of Science databases and bibliometric tools, has two main objectives. First, it identifies trends in AI-based scheduling solutions and the most common AI techniques. Second, it assesses the real impact of AI on production scheduling in real industrial settings. This study shows that particle swarm optimization, neural networks, and reinforcement learning are the most widely used techniques to solve scheduling problems. AI solutions have reduced production costs, increased energy efficiency, and improved scheduling in practical applications. AI is increasingly critical in addressing the evolving challenges in contemporary manufacturing environments.

show abstract

Section: Reinforcement Learningmentioning

confidence: 99%