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,
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author = "Yong Zhou and Jian-Jun Yang and Lian-Yu Zheng",
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journal = "IEEE Access",
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title = "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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year = "2019",
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volume = "7",
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pages = "21147--21176",
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keywords = "genetic algorithms, genetic programming, Job shop
scheduling, Processor scheduling, Manufacturing,
Dispatching, Dynamic scheduling, Scheduling, flexible
job shop, multi-agent, hyper-heuristics",
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DOI = "doi:10.1109/ACCESS.2019.2897603",
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ISSN = "2169-3536",
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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.",
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notes = "Also known as \cite{8635479}",
- }
Genetic Programming entries for
Yong Zhou
Jian-Jun Yang
Lian-Yu Zheng
Citations