Genetic Programming with Delayed Routing for Multi-Objective Dynamic Flexible Job Shop Scheduling
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Binzi-Xu:EC,
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author = "Binzi Xu and Yi Mei and Yan Wang and Zhicheng Ji and
Mengjie Zhang",
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title = "Genetic Programming with Delayed Routing for
Multi-Objective Dynamic Flexible Job Shop Scheduling",
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journal = "Evolutionary Computation",
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year = "2021",
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volume = "29",
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number = "1",
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pages = "75--105",
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month = "Spring",
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keywords = "genetic algorithms, genetic programming, dynamic
flexible job shop scheduling, dispatching rule
discovery, delayed routing, energy efficiency",
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ISSN = "1063-6560",
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URL = "https://meiyi1986.github.io/publication/xu-2020-genetic/xu-2020-genetic.pdf",
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URL = "https://meiyi1986.github.io/publication/xu-2020-genetic/",
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DOI = "doi:10.1162/evco_a_00273",
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size = "31 pages",
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abstract = "Dynamic Flexible Job Shop Scheduling (DFJSS) is an
important and challenging problem, and can have
multiple conflicting objectives. Genetic Programming
HyperHeuristic (GPHH) is a promising approach to fast
respond to the dynamic and unpredictable events in
DFJSS. A GPHH algorithm evolves dispatching rules (DRs)
that are used to make decisions during the scheduling
process (i.e. the so-called heuristic template). In
DFJSS, there are two kinds of scheduling decisions: the
routing decision that allocates each operation to a
machine to process it, and the sequencing decision that
selects the next job to be processed by each idle
machine. The traditional heuristic template makes both
routing and sequencing decisions in a non-delay manner,
which may have limitations in handling the dynamic
environment. we propose a novel heuristic template that
delays the routing decisions rather than making them
immediately. This way, all the decisions can be made
under the latest and more accurate information. We
propose three different delayed routing strategies, and
automatically evolve the rules in the heuristic
template by GPHH. We evaluate the newly proposed GPHH
with Delayed Routing (GPHH-DR) on a multi-objective
DFJSS that optimises the energy efficiency and mean
tardiness. The experimental results show that GPHH-DR
significantly outperformed the state-of-the-art GPHH
methods. We further demonstrated the efficacy of the
proposed heuristic template with delayed routing, which
suggests the importance of delaying the routing
decisions.",
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notes = "School of Electrical Engineering, Anhui Polytechnic
University, Wuhu, China
",
- }
Genetic Programming entries for
Binzi Xu
Yi Mei
Yan Wang
Zhicheng Ji
Mengjie Zhang
Citations