Genetic programming with local search to evolve priority rules for scheduling jobs on a machine with time-varying capacity

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

@Article{GILGALA:2021:SEC,

• author = "Francisco J. Gil-Gala and Maria R. Sierra and Carlos Mencia and Ramiro Varela",
• title = "Genetic programming with local search to evolve priority rules for scheduling jobs on a machine with time-varying capacity",
• journal = "Swarm and Evolutionary Computation",
• year = "2021",
• volume = "66",
• pages = "100944",
• month = oct,
• note = "Special Issue on Memetic Computing: Accelerating Optimization Heuristics with Problem-Dependent Local Search Methods",
• keywords = "genetic algorithms, genetic programming, One machine scheduling, Priority rules, Local search, Memetic algorithm",
• ISSN = "2210-6502",
• URL = "https://www.sciencedirect.com/science/article/pii/S2210650221001061",
• DOI = "doi:10.1016/j.swevo.2021.100944",
• size = "13 pages",
• abstract = "Priority rules combined with schedule generation schemes are a usual approach to online scheduling. These rules are commonly designed by experts on the problem domain. However, some automatic method may be better as it could capture some characteristics of the problem that are not evident to the human eye. Furthermore, automatic methods could devise priority rules adapted to particular sets of instances of the problem at hand. In this paper we propose a Memetic Algorithm, which combines a Genetic Program and a Local Search algorithm, to evolve priority rules for the problem of scheduling a set of jobs on a machine with time-varying capacity. We propose a number of neighbourhood structures that are specifically designed to this problem. These structures were analyzed theoretically and also experimentally on the version of the problem with tardiness minimization, which provided interesting insights on this problem. The results of the experimental study show that a proper selection and combination of neighbourhood structures allows the Memetic Algorithm to outperform previous approaches to the same problem",
• notes = "Department of Computer Science, University of Oviedo, Gijon 33204, Spain",

}

Genetic Programming entries for Francisco Javier Gil Gala Maria Rita Sierra Sanchez Carlos Mencia Cascallana Ramiro Varela Arias

Citations