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Scheduling problems with position dependent job processing times: computational complexity results

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  • Radosław Rudek

Abstract

In this paper, we analyse single machine scheduling problems with learning and aging effects to minimize one of the following objectives: the makespan with release dates, the maximum lateness and the number of late jobs. The phenomena of learning and aging are modeled by job processing times described by non-increasing (learning) or non-decreasing (aging) functions dependent on the number of previously processed jobs, i.e., a job position in a sequence. We prove that the considered problems are strongly NP-hard even if job processing times are described by simple linear functions dependent on a number of processed jobs. Additionally, we show a property of equivalence between problems with learning and aging models. We also prove that if the function describing decrease/increase of a job processing time is the same for each job then the problems with the considered objectives are polynomially solvable even if the function is arbitrary. Therefore, we determine the boundary between polynomially solvable and strongly NP-hard cases. Copyright The Author(s) 2012

Suggested Citation

  • Radosław Rudek, 2012. "Scheduling problems with position dependent job processing times: computational complexity results," Annals of Operations Research, Springer, vol. 196(1), pages 491-516, July.
    • Handle: RePEc:spr:annopr:v:196:y:2012:i:1:p:491-516:10.1007/s10479-012-1098-1
    DOI: 10.1007/s10479-012-1098-1
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    References listed on IDEAS

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    Cited by:

    1. Baruch Mor, 2022. "Minmax common flow-allowance problems with convex resource allocation and position-dependent workloads," Journal of Combinatorial Optimization, Springer, vol. 43(1), pages 79-97, January.
    2. Kinable, Joris & Cire, Andre A. & van Hoeve, Willem-Jan, 2017. "Hybrid optimization methods for time-dependent sequencing problems," European Journal of Operational Research, Elsevier, vol. 259(3), pages 887-897.
    3. Yashar Ahmadov & Petri Helo, 2018. "A cloud based job sequencing with sequence-dependent setup for sheet metal manufacturing," Annals of Operations Research, Springer, vol. 270(1), pages 5-24, November.
    4. Lin-Hui Sun & Kai Cui & Ju-Hong Chen & Jun Wang & Xian-Chen He, 2013. "Research on permutation flow shop scheduling problems with general position-dependent learning effects," Annals of Operations Research, Springer, vol. 211(1), pages 473-480, December.
    5. Peng Liu & Xiaoli Wang, 2017. "Maximum Lateness Scheduling on Two-Person Cooperative Games with Variable Processing Times and Common Due Date," Journal of Optimization, Hindawi, vol. 2017, pages 1-7, April.
    6. Gara-Ali, Ahmed & Finke, Gerd & Espinouse, Marie-Laure, 2016. "Parallel-machine scheduling with maintenance: Praising the assignment problem," European Journal of Operational Research, Elsevier, vol. 252(1), pages 90-97.
    7. Markó Horváth & Tamás Kis, 2020. "Polyhedral results for position-based scheduling of chains on a single machine," Annals of Operations Research, Springer, vol. 284(1), pages 283-322, January.
    8. Adam Janiak & Mikhail Kovalyov & Maciej Lichtenstein, 2013. "Strong NP-hardness of scheduling problems with learning or aging effect," Annals of Operations Research, Springer, vol. 206(1), pages 577-583, July.
    9. Baruch Mor & Gur Mosheiov, 2018. "A note: minimizing total absolute deviation of job completion times on unrelated machines with general position-dependent processing times and job-rejection," Annals of Operations Research, Springer, vol. 271(2), pages 1079-1085, December.

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