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Journal of Scheduling

, Volume 22, Issue 6, pp 691–707 | Cite as

Single-machine scheduling with job-dependent machine deterioration

  • Wenchang Luo
  • Yao Xu
  • Weitian Tong
  • Guohui LinEmail author
Article
  • 119 Downloads

Abstract

We consider the single-machine scheduling problem with job-dependent machine deterioration. In the problem, we are given a single machine with an initial nonnegative maintenance level, and a set of jobs each with a non-preemptive processing time and a machine deterioration. Such a machine deterioration quantifies the decrement in the machine maintenance level after processing the job. To avoid a machine breakdown, one should guarantee a nonnegative maintenance level at any time point, and whenever necessary, a maintenance activity must be allocated for restoring the machine maintenance level. The goal of the problem is to schedule the jobs and the maintenance activities such that the total completion time of jobs is minimized. There are two variants of maintenance activities: In the partial maintenance case, each activity can be allocated to increase the machine maintenance level to any level not exceeding the maximum; in the full maintenance case, every activity must be allocated to increase the machine maintenance level to the maximum. In a recent work, the problem in the full maintenance case was proven NP-hard; several special cases of the problem in the partial maintenance case were shown to be solvable in polynomial time, but the complexity of the general problem was left open. In this paper we first prove that the problem in the partial maintenance case is binary NP-hard, thus settling the open problem; we then design a 2-approximation algorithm and a branch-and-bound exact search algorithm. Computational experiments are conducted for the two algorithms to examine their practical performance.

Keywords

Scheduling Machine deterioration Maintenance Binary NP-hard Approximation algorithm 

Notes

Acknowledgements

The authors are grateful to the reviewers’ valuable comments which improved the manuscript. W.L. was supported by K.C. Wong Magna Fund of Ningbo University, the China Scholarship Council (Grant No. 201408330402), the Humanities and Social Sciences Planning Foundation of the Ministry of Education (Grant No. 18YJA630077), Zhejiang Provincial Natural Science Foundation (Grant No. LY19A010005), Natural Science Foundation of China (Grant No. 11971252) and the Ningbo Natural Science Foundation (Grant No. 2018A610198). W.T. was supported in part by funds from the Office of the Vice President for Research & Economic Development at Georgia Southern University. G.L. was supported by NSERC Canada.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Mathematics and StatisticsNingbo UniversityNingboChina
  2. 2.Department of Computing ScienceUniversity of AlbertaEdmontonCanada
  3. 3.Department of Computer ScienceGeorgia Southern UniversityStatesboroUSA

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