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

Jens Rocholl, Lars Mönch, John Fowler

Research output: Contribution to journalArticle

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 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.

Original languageEnglish (US)
JournalJournal of Business Economics
DOIs
StateAccepted/In press - Jan 1 2020

Keywords

  • Batch processing
  • Energy consumption
  • Grouping genetic algorithm
  • Scheduling
  • Semiconductor manufacturing
  • Total weighted tardiness

ASJC Scopus subject areas

  • Business and International Management
  • Economics and Econometrics

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