Genetic Programming for Job Shop Scheduling

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InCollection{nguyen:2019:ESIA,

• author = "Su Nguyen and Mengjie Zhang and Mark Johnston and Kay Chen Tan",
• title = "Genetic Programming for Job Shop Scheduling",
• booktitle = "Evolutionary and Swarm Intelligence Algorithms",
• year = "2019",
• editor = "Jagdish Chand Bansal and Pramod Kumar Singh and Nikhil R. Pal",
• volume = "779",
• series = "Studies in Computational Intelligence",
• chapter = "8",
• pages = "143--167",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, job shop scheduling, heuristic",
• isbn13 = "978-3-319-91339-1",
• URL = "https://eprints.worc.ac.uk/id/eprint/7579",
• URL = "https://figshare.com/articles/chapter/Genetic_programming_for_job_shop_scheduling/13158287/files/25303547.pdf",
• URL = "http://link.springer.com/chapter/10.1007/978-3-319-91341-4_8",
• DOI = "doi:10.1007/978-3-319-91341-4_8",
• size = "25 pages",
• abstract = "Designing effective scheduling rules or heuristics for a manufacturing system such as job shops is not a trivial task. In the early stage, scheduling experts rely on their experiences to develop dispatching rules and further improve them through trials-and-errors, sometimes with the help of computer simulations. In recent years, automated design approaches have been applied to develop effective dispatching rules for job shop scheduling (JSS). Genetic programming (GP) is currently the most popular approach for this task. The goal of this chapter is to summarise existing studies in this field to provide an overall picture to interested researchers. Then, we demonstrate some recent ideas to enhance the effectiveness of GP for JSS and discuss interesting research topics for future studies.",

}

Genetic Programming entries for Su Nguyen Mengjie Zhang Mark Johnston Kay Chen Tan

Citations