Evolving control rules for a dual-constrained job scheduling scenario

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Branke:2016:WSC,

• author = "Juergen Branke and Matthew J. Groves and Torsten Hildebrandt",
• booktitle = "2016 Winter Simulation Conference (WSC)",
• title = "Evolving control rules for a dual-constrained job scheduling scenario",
• year = "2016",
• pages = "2568--2579",
• abstract = "Dispatching rules are often used for scheduling in semiconductor manufacturing due to the complexity and stochasticity of the problem. In the past, simulation-based Genetic Programming has been shown to be a powerful tool to automate the time-consuming and expensive process of designing such rules. However, the scheduling problems considered were usually only constrained by the capacity of the machines. In this paper, we extend this idea to dual-constrained flow shop scheduling, with machines and operators for loading and unloading to be scheduled simultaneously. We show empirically on a small test problem with parallel workstations, re-entrant flows and dynamic stochastic job arrival that the approach is able to generate dispatching rules that perform significantly better than benchmark rules from the literature.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/WSC.2016.7822295",
• month = dec,
• notes = "Also known as \cite{7822295}",

}

Genetic Programming entries for Jurgen Branke Matthew J Groves Torsten Hildebrandt

Citations