Unbounded parallel-batch scheduling under agreeable release and processing to minimize total weighted number of tardy jobs

Yuan Gao and Jinjiang Yuan ()
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Yuan Gao: Zhengzhou University
Jinjiang Yuan: Zhengzhou University

Journal of Combinatorial Optimization, 2019, vol. 38, issue 3, No 4, 698-711

Abstract: Abstract We study the unbounded parallel-batch scheduling problem with the jobs having agreeable release dates and processing times to minimize the total weighted number of tardy jobs. In this problem, we consider two types of jobs: 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 show that (1) the problems are binary NP-hard, (2) the problems are solvable in pseudo-polynomial times, and when the number of weights is a constant, the problems are solvable in polynomial times, and (3) the problems have a fully polynomial-time approximation scheme.

Keywords: Parallel-batch scheduling; Release dates; Drop-line jobs; Polynomial-time approximation scheme (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10878-019-00407-z

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