Importance-Aware Genetic Programming for Automated Scheduling Heuristics Learning in Dynamic Flexible Job Shop Scheduling
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{DBLP:conf/ppsn/ZhangMNZ22,
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author = "Fangfang Zhang and Yi Mei and Su Nguyen and
Mengjie Zhang",
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title = "Importance-Aware Genetic Programming for Automated
Scheduling Heuristics Learning in Dynamic Flexible Job
Shop Scheduling",
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booktitle = "Parallel Problem Solving from Nature - PPSN XVII -
17th International Conference, PPSN 2022, Proceedings,
Part II",
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year = "2022",
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editor = "Guenter Rudolph and Anna V. Kononova and
Hernan E. Aguirre and Pascal Kerschke and Gabriela Ochoa and
Tea Tusar",
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volume = "13399",
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series = "Lecture Notes in Computer Science",
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pages = "48--62",
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address = "Dortmund, Germany",
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month = sep # " 10-14",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming,
Importance-aware scheduling heuristics learning,
Hyper-heuristic, Dynamic flexible job shop scheduling",
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timestamp = "Tue, 16 Aug 2022 16:15:42 +0200",
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biburl = "https://dblp.org/rec/conf/ppsn/ZhangMNZ22.bib",
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bibsource = "dblp computer science bibliography, https://dblp.org",
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isbn13 = "978-3-031-14720-3",
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DOI = "doi:10.1007/978-3-031-14721-0_4",
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abstract = "Dynamic flexible job shop scheduling (DFJSS) is a
critical and challenging problem in production
scheduling such as order picking in the warehouse.
Given a set of machines and a number of jobs with a
sequence of operations, DFJSS aims to generate
schedules for completing jobs to minimise total costs
while reacting effectively to dynamic changes. Genetic
programming, as a hyper-heuristic approach, has been
widely used to learn scheduling heuristics for DFJSS
automatically. A scheduling heuristic in DFJSS includes
a routing rule for machine assignment and a sequencing
rule for operation sequencing. However, existing
studies assume that the routing and sequencing are
equally important, which may not be true in real-world
applications. This paper aims to propose an
importance-aware GP algorithm for automated scheduling
heuristics learning in DFJSS. Specifically, we first
design a rule importance measure based on the fitness
improvement achieved by the routing rule and the
sequencing rule across generations. Then, we develop an
adaptive resource allocation strategy to give more
resources for learning the more important rules. The
results show that the proposed importance-aware GP
algorithm can learn significantly better scheduling
heuristics than the compared algorithms. The
effectiveness of the proposed algorithm is realised by
the proposed strategies for detecting rule importance
and allocating resources. Particularly, the routing
rules play a more important role than the sequencing
rules in the examined DFJSS scenarios.",
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notes = "PPSN2022",
- }
Genetic Programming entries for
Fangfang Zhang
Yi Mei
Su Nguyen
Mengjie Zhang
Citations