Bi-criteria parallel batch machine scheduling to minimize total weighted tardiness and electricity cost

Jens Rocholl, Lars Mönch () and John Fowler
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Jens Rocholl: University of Hagen
Lars Mönch: University of Hagen
John Fowler: Arizona State University

Journal of Business Economics, 2020, vol. 90, issue 9, No 4, 1345-1381

Abstract: Abstract A bi-criteria scheduling problem for parallel identical batch processing machines in semiconductor wafer fabrication facilities is studied. Only jobs belonging to the same family can be batched together. The performance measures are the total weighted tardiness and the electricity cost where a time-of-use (TOU) tariff is assumed. Unequal ready times of the jobs and non-identical job sizes are considered. A mixed integer linear program (MILP) is formulated. We analyze the special case where all jobs have the same size, all due dates are zero, and the jobs are available at time zero. Properties of Pareto-optimal schedules for this special case are stated. They lead to a more tractable MILP. We design three heuristics based on grouping genetic algorithms that are embedded into a non-dominated sorting genetic algorithm II framework. Three solution representations are studied that allow for choosing start times of the batches to take into account the energy consumption. We discuss a heuristic that improves a given near-to-optimal Pareto front. Computational experiments are conducted based on randomly generated problem instances. The $$ \varepsilon $$ ε -constraint method is used for both MILP formulations to determine the true Pareto front. For large-sized problem instances, we apply the genetic algorithms (GAs). Some of the GAs provide high-quality solutions.

Keywords: Scheduling; Batch processing; Semiconductor manufacturing; Energy consumption; Total weighted tardiness; Grouping genetic algorithm (search for similar items in EconPapers)
JEL-codes: C44 C61 L63 M11 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s11573-020-00970-6

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