A learning-based two-stage optimization method for customer order scheduling
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{SHI:2021:COR,
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author = "Zhongshun Shi and Hang Ma and Meiheng Ren and
Tao Wu and Andrew J. Yu",
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title = "A learning-based two-stage optimization method for
customer order scheduling",
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journal = "Computer \& Operations Research",
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volume = "136",
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pages = "105488",
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year = "2021",
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ISSN = "0305-0548",
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DOI = "doi:10.1016/j.cor.2021.105488",
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URL = "https://www.sciencedirect.com/science/article/pii/S0305054821002355",
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keywords = "genetic algorithms, genetic programming, Customer
order scheduling, Artificial intelligence, Dispatching
rules, Heuristics",
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abstract = "This paper addresses the customer order scheduling
problem in parallel production environment commonly
appearing in the pharmaceutical and paper industries.
The problem aims to minimize the total completion time
of the orders with their jobs processed on dedicated
machines in parallel. To deal with the computational
challenge of large-scale problems, we propose a
learning-based two-stage optimization method consisting
of a learned dispatching rule in the first stage and an
adaptive local search in the second stage. The new
dispatching rules are automatically generated by the
proposed feature-enhanced genetic programming method in
an off-line learning manner. Based on the high-quality
initial solutions provided by the learned dispatching
rule, we develop an adaptive local search to further
improve the solution quality. Numerical results
indicate the superiority of the learned dispatching
rule and show the proposed two-stage optimization
method significantly outperforms state-of-the-art
methods in the literature",
- }
Genetic Programming entries for
Tony Zhongshun Shi
Hang Ma
Meiheng Ren
Tao Wu
Andrew J Yu
Citations