Min–Max Scheduling of Batch or Drop-Line Jobs Under Agreeable Release and Processing Times

Yuan Gao
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Yuan Gao: School of Mathematics and Statistics, Zhengzhou University, Zhengzhou, Henan 450001, P. R. China2School of Information Engineering, Zhengzhou University, Zhengzhou, Henan 450001, P. R. China

Asia-Pacific Journal of Operational Research (APJOR), 2022, vol. 39, issue 02, 1-15

Abstract: We study the Pareto optimization scheduling on an unbounded parallel-batch machine with jobs having agreeable release dates and processing times for minimizing makespan and maximum cost simultaneously. The jobs considered in this paper are of two types: batch jobs and drop-line jobs. For batch jobs, the completion time of a job is given by the completion time of the batch containing this job. For drop-line jobs, the completion time of a job is given by the starting time of the batch containing this job plus the processing time of this job. For both of batch jobs and drop-line jobs, we present polynomial-time algorithms for finding all Pareto optimal points.

Keywords: Parallel-batch scheduling; release dates; pareto optimization; polynomial-time algorithm (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S0217595921500238

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