An ant colony optimization-based hyper-heuristic with genetic programming approach for a hybrid flow shop scheduling problem

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

@InProceedings{Chen:2015:CECb,

• author = "Lin Chen and Hong Zheng and Dan Zheng and Dongni Li",
• booktitle = "2015 IEEE Congress on Evolutionary Computation (CEC)",
• title = "An ant colony optimization-based hyper-heuristic with genetic programming approach for a hybrid flow shop scheduling problem",
• year = "2015",
• pages = "814--821",
• isbn13 = "978-1-4799-7491-7",
• abstract = "The problem of a k-stage hybrid flow shop (HFS) with one stage composed of non-identical batch processing machines and the others consisting of non-identical single processing machines is analysed in the context of the equipment manufacturing industry. Due to the complexity of the addressed problem, a hyper-heuristic which combines heuristic generation and heuristic search is proposed to solve the problem. For each sub-problem, i.e., part assignment, part sequencing and batch formation, heuristic rules are first generated by genetic programming (GP) off-line and then selected by ant colony optimisation (ACO) correspondingly. Finally, the scheduling solutions are obtained through the above generated combinatorial heuristic rules. Aiming at minimizing the total weighted tardiness of parts, a comparison experiment with the other hyper-heuristic for the same HFS problem is conducted. The result has shown that the proposed algorithm has advantages over the other method with respect to the total weighted tardiness.",
• keywords = "genetic algorithms, genetic programming, ant colony optimization, ACO, scheduling, discrete event systems",
• DOI = "doi:10.1109/CEC.2015.7256975",
• ISSN = "1089-778X",
• month = may,
• notes = "Also known as \cite{7256975}",

}

Genetic Programming entries for Lin Chen Hong Zheng Dan Zheng Dongni Li

Citations