A Hybrid Genetic Programming Algorithm for Automated Design of Dispatching Rules

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@Article{Su_Nguyen:EC,

• author = "Su Nguyen and Yi Mei and Bing Xue and Mengjie Zhang",
• title = "A Hybrid Genetic Programming Algorithm for Automated Design of Dispatching Rules",
• journal = "Evolutionary Computation",
• year = "2019",
• volume = "27",
• number = "3",
• pages = "467--496",
• month = "Fall",
• keywords = "genetic algorithms, genetic programming, job shop, production scheduling, hyper-heuristics",
• ISSN = "1063-6560",
• URL = "https://homepages.ecs.vuw.ac.nz/~xuebing/Papers/ECJ18-Su.pdf",
• DOI = "doi:10.1162/evco_a_00230",
• size = "31 pages",
• abstract = "Designing effective dispatching rules for production systems is a difficult and time consuming task if it is done manually. In the last decade, the growth of computing power, advanced machine learning, and optimisation techniques has made the automated design of dispatching rules possible and automatically discovered rules are competitive or outperform existing rules developed by researchers. Genetic programming is one of the most popular approaches to discovering dispatching rules in the literature, especially for complex production systems. However, the large heuristic search space may restrict genetic programming from finding near optimal dispatching rules. This paper develops a new hybrid genetic programming algorithm for dynamic job shop scheduling based on a new representation, a new local search heuristic, and efficient fitness evaluators. Experiments show that the new method is effective regarding the quality of evolved rules. Moreover, evolved rules are also significantly smaller and contain more relevant attributes.",

}

Genetic Programming entries for Su Nguyen Yi Mei Bing Xue Mengjie Zhang

Citations