Learning Priority Indices for Energy-Aware Scheduling of Jobs on Batch Processing Machines

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@Article{Schorn:2024:SM,

• author = "Daniel Sascha Schorn and Lars Moench",
• journal = "IEEE Transactions on Semiconductor Manufacturing",
• title = "Learning Priority Indices for Energy-Aware Scheduling of Jobs on Batch Processing Machines",
• year = "2024",
• volume = "37",
• number = "1",
• pages = "3--15",
• abstract = "A scheduling problem for parallel batch processing machines (BPMs) with jobs having unequal ready times in semiconductor wafer fabrication facilities (wafer fabs) is studied in this paper. A blended objective function combining the total weighted tardiness (TWT) and the total electricity cost (TEC) under a time-of-use (TOU) tariff is considered. A genetic programming (GP) procedure is designed to automatically discover priority indices for a heuristic scheduling framework. Results of computational experiments are reported that demonstrate that the learnt priority indices lead to high-quality schedules in a short amount of computing time.",
• keywords = "genetic algorithms, genetic programming, Job shop scheduling, Processor scheduling, Schedules, Dispatching, Tariffs, Semiconductor device measurement, Batch production systems, Learning, priority indices, batch processing machines, energy-aware scheduling",
• DOI = "doi:10.1109/TSM.2023.3326865",
• ISSN = "1558-2345",
• month = feb,
• notes = "Also known as \cite{10293187}",

}

Genetic Programming entries for Daniel Sascha Schorn Lars Moench

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