Multi-agent based hyper-heuristics for multi-objective flexible job shop scheduling: A case study in an aero-engine blade manufacturing plant

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@Article{Zhou:IEEEAccess,

• author = "Yong Zhou and Jian-Jun Yang and Lian-Yu Zheng",
• journal = "IEEE Access",
• title = "Multi-agent based hyper-heuristics for multi-objective flexible job shop scheduling: A case study in an aero-engine blade manufacturing plant",
• year = "2019",
• volume = "7",
• pages = "21147--21176",
• keywords = "genetic algorithms, genetic programming, Job shop scheduling, Processor scheduling, Manufacturing, Dispatching, Dynamic scheduling, Scheduling, flexible job shop, multi-agent, hyper-heuristics",
• DOI = "doi:10.1109/ACCESS.2019.2897603",
• ISSN = "2169-3536",
• abstract = "In the present work, a case study focusing on multi-objective flexible job shop scheduling problem (MO-FJSP) in an aero-engine blade manufacturing plant is presented. The problem considered in this study involves many attributes, including working calendar, due dates and lot size. Moreover, dynamic events occur frequently in the shop-floor, making the problem more challenging and requiring real-time responses. Therefore, the priority-based methods are more suitable than the computationally intensive search-based methods for online scheduling. However, developing an effective heuristic for online scheduling problem is a tedious work even for domain experts. Furthermore, the domain knowledge of practical production scheduling needs to be integrated into the algorithm to guide the search direction, accelerate the convergence of the algorithm and improve the solution quality. To this end, three multi-agent based hyper-heuristics (MAHH) integrated with the prior knowledge of the shop floor are proposed to evolve scheduling policies (SPs) for online scheduling problem. To evaluate the performance of evolved SPs, a 5-fold cross-validation method which is frequently used in machine learning is adopted to avoid the overfitting problem. Both the training and test results demonstrate that the bottleneck-agent based hyper-heuristic method produces the best result among the three MAHH methods. Furthermore, both the effectiveness and the efficiency of the evolved SPs are verified by comparison with the well-known heuristics and two multi-objective particle swarm optimization (MOPSO) algorithms on the practical case. The proposed method has been embedded into the manufacturing execution system (MES) that is built on JAVA and successfully applied in several manufacturing plants.",
• notes = "Also known as \cite{8635479}",

}

Genetic Programming entries for Yong Zhou Jian-Jun Yang Lian-Yu Zheng

Citations