Learning Reusable Initial Solutions for Multi-Objective Order Acceptance and Scheduling Problems with Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{nguyen:2013:EuroGP,
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author = "Su Nguyen and Mengjie Zhang and Mark Johnston and
Kay Chen Tan",
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title = "Learning Reusable Initial Solutions for
Multi-Objective Order Acceptance and Scheduling
Problems with Genetic Programming",
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booktitle = "Proceedings of the 16th European Conference on Genetic
Programming, EuroGP 2013",
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year = "2013",
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month = "3-5 " # apr,
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editor = "Krzysztof Krawiec and Alberto Moraglio and Ting Hu and
A. Sima Uyar and Bin Hu",
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series = "LNCS",
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volume = "7831",
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publisher = "Springer Verlag",
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address = "Vienna, Austria",
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pages = "157--168",
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organisation = "EvoStar",
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keywords = "genetic algorithms, genetic programming, scheduling,
multiple objective",
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isbn13 = "978-3-642-37206-3",
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DOI = "doi:10.1007/978-3-642-37207-0_14",
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abstract = "Order acceptance and scheduling (OAS) is an important
issue in make-to-order production systems that decides
the set of orders to accept and the sequence in which
these accepted orders are processed to increase total
revenue and improve customer satisfaction. This paper
aims to explore the Pareto fronts of trade-off
solutions for a multi-objective OAS problem. Due to its
complexity, solving this problem is challenging. A
two-stage learning/optimising (2SLO) system is proposed
in this paper to solve the problem. The novelty of this
system is the use of genetic programming to evolve a
set of scheduling rules that can be reused to
initialise populations of an evolutionary
multi-objective optimisation (EMO) method. The
computational results show that 2SLO is more effective
than the pure EMO method. Regarding maximising the
total revenue, 2SLO is also competitive as compared to
other optimisation methods in the literature.",
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notes = "Part of \cite{Krawiec:2013:GP} EuroGP'2013 held in
conjunction with EvoCOP2013, EvoBIO2013, EvoMusArt2013
and EvoApplications2013",
- }
Genetic Programming entries for
Su Nguyen
Mengjie Zhang
Mark Johnston
Kay Chen Tan
Citations