Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-37207-0_14
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dc.titleLearning reusable initial solutions for multi-objective order acceptance and scheduling problems with genetic programming
dc.contributor.authorNguyen, S.
dc.contributor.authorZhang, M.
dc.contributor.authorJohnston, M.
dc.contributor.authorTan, K.C.
dc.date.accessioned2014-10-07T04:46:25Z
dc.date.available2014-10-07T04:46:25Z
dc.date.issued2013
dc.identifier.citationNguyen, S.,Zhang, M.,Johnston, M.,Tan, K.C. (2013). Learning reusable initial solutions for multi-objective order acceptance and scheduling problems with genetic programming. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7831 LNCS : 157-168. ScholarBank@NUS Repository. <a href="https://doi.org/10.1007/978-3-642-37207-0_14" target="_blank">https://doi.org/10.1007/978-3-642-37207-0_14</a>
dc.identifier.isbn9783642372063
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/83894
dc.description.abstractOrder 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. © 2013 Springer-Verlag.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/978-3-642-37207-0_14
dc.sourceScopus
dc.subjectgenetic programming
dc.subjectmultiple objective
dc.subjectscheduling
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1007/978-3-642-37207-0_14
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume7831 LNCS
dc.description.page157-168
dc.identifier.isiutNOT_IN_WOS
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