Learning Dispatching Rules for Energy-Aware Scheduling of Jobs on a Single Batch Processing Machine

Created by W.Langdon from gp-bibliography.bib Revision:1.8612

@InProceedings{Schorn:2022:WSC,

• author = "Daniel Sascha Schorn and Lars Moench",
• booktitle = "2022 Winter Simulation Conference (WSC)",
• title = "Learning Dispatching Rules for Energy-Aware Scheduling of Jobs on a Single Batch Processing Machine",
• year = "2022",
• pages = "3360--3371",
• abstract = "In this paper, we consider a scheduling problem for a single batch processing machine in semiconductor wafer fabrication facilities (wafer fabs). An integrated objective function that combines the total weighted tardiness (TWT) and the electricity cost (EC) is considered. A time-of-use (TOU) tariff is assumed. A genetic programming (GP) procedure is proposed to automatically discover dispatching rules for list scheduling approaches. Results of designed computational experiments demonstrate that the learned dispatching rules lead to high-quality schedules in a short amount of computing time.",
• keywords = "genetic algorithms, genetic programming, Fabrication, Schedules, Costs, Processor scheduling, Computational modeling, Batch production systems, Tariffs",
• DOI = "10.1109/WSC57314.2022.10015511",
• ISSN = "1558-4305",
• month = dec,
• notes = "Also known as \cite{10015511}",

}

Genetic Programming entries for Daniel Sascha Schorn Lars Moench

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