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Scheduling of Jobs with Multiple Weights on a Single Machine for Minimizing the Total Weighted Number of Tardy Jobs

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  • Shuen Guo

    (School of Mathematics and Statistics, Zhengzhou University, Zhengzhou 450001, China)

  • Hao Lang

    (Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hong Kong SAR, China)

  • Hanxiang Zhang

    (Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hong Kong SAR, China)

Abstract

We consider the scheduling of jobs with multiple weights on a single machine for minimizing the total weighted number of tardy jobs. In this setting, each job has m weights (or equivalently, the jobs have m weighting vectors), and thus we have m criteria, each of which is to minimize the total weighted number of tardy jobs under a corresponding weighting vector of the jobs. For this scheduling model, the feasibility problem aims to find a feasible schedule such that each criterion is upper bounded by its threshold value, and the Pareto scheduling problem aims to find all the Pareto-optimal points and for each one a corresponding Pareto-optimal schedule. Although the two problems have not been studied before, it is implied in the literature that both of them are unary NP-hard when m is an arbitrary number. We show in this paper that, in the case where m is a fixed number, the two problems are solvable in pseudo-polynomial time, the feasibility problem admits a dual-fully polynomial-time approximation scheme, and the Pareto-scheduling problem admits a fully polynomial-time approximation scheme.

Suggested Citation

  • Shuen Guo & Hao Lang & Hanxiang Zhang, 2023. "Scheduling of Jobs with Multiple Weights on a Single Machine for Minimizing the Total Weighted Number of Tardy Jobs," Mathematics, MDPI, vol. 11(4), pages 1-19, February.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:4:p:1013-:d:1070715
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    References listed on IDEAS

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    2. Yi-Chun Wang & Si-Han Wang & Ji-Bo Wang, 2023. "Resource Allocation Scheduling with Position-Dependent Weights and Generalized Earliness–Tardiness Cost," Mathematics, MDPI, vol. 11(1), pages 1-11, January.
    3. Shi-Sheng Li & Jin-Jiang Yuan, 2020. "Single-machine scheduling with multi-agents to minimize total weighted late work," Journal of Scheduling, Springer, vol. 23(4), pages 497-512, August.
    4. Jinjiang Yuan, 2017. "Unary NP-hardness of minimizing the number of tardy jobs with deadlines," Journal of Scheduling, Springer, vol. 20(2), pages 211-218, April.
    5. Ruyan He & Jinjiang Yuan & C. T. Ng & T. C. E. Cheng, 2021. "Two-agent preemptive Pareto-scheduling to minimize the number of tardy jobs and total late work," Journal of Combinatorial Optimization, Springer, vol. 41(2), pages 504-525, February.
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    Cited by:

    1. Adrian Marius Deaconu & Daniel Tudor Cotfas & Petru Adrian Cotfas, 2023. "Advanced Optimization Methods and Applications," Mathematics, MDPI, vol. 11(9), pages 1-7, May.

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