Genetic programming-based hyper-heuristic approach for solving dynamic job shop scheduling problem with extended technical precedence constraints
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gp-bibliography.bib Revision:1.7975
- @Article{FAN:2021:COR,
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author = "Huali Fan and Hegen Xiong and Mark Goh",
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title = "Genetic programming-based hyper-heuristic approach for
solving dynamic job shop scheduling problem with
extended technical precedence constraints",
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journal = "Computer \& Operations Research",
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volume = "134",
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pages = "105401",
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year = "2021",
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month = oct,
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keywords = "genetic algorithms, genetic programming, Dynamic job
shop scheduling, Dispatching rules, Hyper-heuristic,
Extended technical precedence constraints",
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ISSN = "0305-0548",
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DOI = "doi:10.1016/j.cor.2021.105401",
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URL = "https://www.sciencedirect.com/science/article/pii/S0305054821001672",
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abstract = "Extended technical precedence constraints (ETPC) in
dynamic job shop scheduling problem (DJSP) are the
precedence constraints existing between different jobs
instead of the conventional technical precedence
constraints existing in the operations of the same job.
This paper presents the mathematical programming model
of the DJSP with ETPC to minimize the mean weighted
tardiness of the jobs. The mathematical model
contributes to the solution and modelling of the DJSP
with ETPC and it is used to solve small-sized problems
to optimality. To solve industry-sized problems, a
constructive heuristic called the dispatching rule (DR)
is employed. This paper investigates the use of genetic
programming (GP) as a hyper-heuristic in the automated
generation of the problem-specific DRs for solving the
problem under consideration. The genetic
programming-based hyper heuristic (GPHH) approach
constructs the DRs which are learned from the training
instances and then verified on the test instances by
the simulation experiments. To enhance the efficiency
of the approach when evolving effective DRs to solve
the problem, the approach is improved with strategies
which consist of a problem-specific attribute selection
for GP and a threshold condition mechanism for fitness
evaluation. The simulation results verify the
effectiveness and efficiency of the evolved DRs to the
problem under consideration by comparing against the
existing classical DRs. The statistical analysis of the
simulation results shows that the evolved DRs
outperform the selected benchmark DRs on the problem
under study. The sensitivity analysis also shows that
the DRs generated by the GPHH approach are robust under
different scheduling performance measures. Moreover,
the effects of the model parameters, including the
percentage of jobs with ETPC and the machine load, on
the performance of the DRs are investigated",
- }
Genetic Programming entries for
Huali Fan
Hegen Xiong
Mark Goh
Citations