Evolving Machine-Specific Dispatching Rules for a Two-Machine Job Shop using Genetic Programming

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

@InProceedings{Hunt:2014:CEC,

• title = "Evolving Machine-Specific Dispatching Rules for a Two-Machine Job Shop using Genetic Programming",
• author = "Rachel Hunt and Mark Johnston and Mengjie Zhang",
• pages = "618--625",
• booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary Computation",
• year = "2014",
• month = "6-11 " # jul,
• editor = "Carlos A. {Coello Coello}",
• address = "Beijing, China",
• ISBN = "0-7803-8515-2",
• keywords = "genetic algorithms, genetic programming, Evolutionary Computation for Planning and Scheduling",
• DOI = "doi:10.1109/CEC.2014.6900655",
• abstract = "Job Shop Scheduling (JSS) involves determining a schedule for processing jobs on machines to optimise some measure of delivery speed or customer satisfaction. We investigate a genetic programming based hyper-heuristic (GPHH) approach to evolving dispatching rules for a two-machine job shop in both static and dynamic environments. In the static case the proposed GPHH method can represent and discover optimal dispatching rules. In the dynamic case we investigate two representations (using a single rule at both machines and evolving a specialised rule for each machine) and the effect of changing the training problem instances throughout evolution. Results show that relative performance of these methods is dependent on the testing instances.",
• notes = "WCCI2014",

}

Genetic Programming entries for Rachel Hunt Mark Johnston Mengjie Zhang

Citations