Genetic Programming with Delayed Routing for Multi-Objective Dynamic Flexible Job Shop Scheduling
Created by W.Langdon from
gp-bibliography.bib Revision:1.7428
- @Article{Binzi-Xu:EC,
-
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",
-
ISSN = "1063-6560",
-
URL = "
https://meiyi1986.github.io/publication/xu-2020-genetic/xu-2020-genetic.pdf",
-
URL = "
https://meiyi1986.github.io/publication/xu-2020-genetic/",
-
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. In this paper, 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