Learning Reusable Initial Solutions for Multi-Objective Order Acceptance and Scheduling Problems with Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{nguyen:2013:EuroGP,

• author = "Su Nguyen and Mengjie Zhang and Mark Johnston and Kay Chen Tan",
• title = "Learning Reusable Initial Solutions for Multi-Objective Order Acceptance and Scheduling Problems with Genetic Programming",
• booktitle = "Proceedings of the 16th European Conference on Genetic Programming, EuroGP 2013",
• year = "2013",
• month = "3-5 " # apr,
• editor = "Krzysztof Krawiec and Alberto Moraglio and Ting Hu and A. Sima Uyar and Bin Hu",
• series = "LNCS",
• volume = "7831",
• publisher = "Springer Verlag",
• address = "Vienna, Austria",
• pages = "157--168",
• organisation = "EvoStar",
• keywords = "genetic algorithms, genetic programming, scheduling, multiple objective",
• isbn13 = "978-3-642-37206-3",
• DOI = "doi: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.",
• 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