Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-37207-0_14
Title: Learning reusable initial solutions for multi-objective order acceptance and scheduling problems with genetic programming
Authors: Nguyen, S.
Zhang, M.
Johnston, M.
Tan, K.C. 
Keywords: genetic programming
multiple objective
scheduling
Issue Date: 2013
Source: Nguyen, 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. https://doi.org/10.1007/978-3-642-37207-0_14
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. © 2013 Springer-Verlag.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/83894
ISBN: 9783642372063
ISSN: 03029743
DOI: 10.1007/978-3-642-37207-0_14
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