Automatic Design of Efficient Heuristics for Two-Stage Hybrid Flow Shop Scheduling
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- @Article{liu:2022:Symmetry,
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author = "Lingxuan Liu and Leyuan Shi",
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title = "Automatic Design of Efficient Heuristics for Two-Stage
Hybrid Flow Shop Scheduling",
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journal = "Symmetry",
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year = "2022",
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volume = "14",
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number = "4",
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pages = "Article No. 632",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "2073-8994",
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URL = "https://www.mdpi.com/2073-8994/14/4/632",
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DOI = "doi:10.3390/sym14040632",
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abstract = "This paper addresses the two-stage hybrid flow shop
scheduling problem with a batch processor in the first
stage and a discrete processor in the second stage.
Incompatible job families and limited buffer size are
considered. This hybrid flow shop configuration
commonly appears in manufacturing operations and the
batch processor is always the bottleneck which breaks
the symmetry of processing time. Since making a
real-time high-quality schedule is challenging, we
focus on the automatic design of efficient heuristics
for this two-stage problem based on the genetic
programming method. We develop a hyper-heuristic
approach to automate the tedious trial-and-error design
process of heuristics. The goal is to generate
efficient dispatching rules for identifying complete
schedules to minimise the total completion time. A
genetic programming with cooperative co-evolution
approach is proposed to evolve the schedule policy
automatically. Numerical results demonstrate that the
proposed approach outperforms both the constructive
heuristic and meta-heuristic algorithms, and is capable
of producing high-quality schedules within seconds.",
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notes = "also known as \cite{sym14040632}",
- }
Genetic Programming entries for
Lingxuan Liu
Leyuan Shi
Citations