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Uniform Parallel Machine Scheduling with Dedicated Machines, Job Splitting and Setup Resources

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  • Jun-Ho Lee

    (School of Business, Konkuk University, Seoul 05029, Korea)

  • Hoon Jang

    (College of Global Business, Korea University, Sejong 30019, Korea)

Abstract

We examine a uniform parallel machine scheduling problem with dedicated machines, job splitting, and limited setup resources for makespan minimization. In this problem, machines have different processing speeds, and each job can only be processed at several designated machines. A job can be split into multiple sections and those sections can be processed on multiple machines simultaneously. Sequence-independent setup times are assumed, and setup operations between jobs require setup operators that are limited. For the problem, we first develop a mathematical optimization model and for large-sized problems a constructive heuristic algorithm is proposed. Finally, we show that the algorithm developed is efficient and provides good solutions by experiments with various scenarios.

Suggested Citation

  • Jun-Ho Lee & Hoon Jang, 2019. "Uniform Parallel Machine Scheduling with Dedicated Machines, Job Splitting and Setup Resources," Sustainability, MDPI, vol. 11(24), pages 1-23, December.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:24:p:7137-:d:297455
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    References listed on IDEAS

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    Cited by:

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    2. Roman Buil & Jesica de Armas & Daniel Riera & Sandra Orozco, 2021. "Optimization of the Real-Time Response to Roadside Incidents through Heuristic and Linear Programming," Mathematics, MDPI, vol. 9(16), pages 1-20, August.
    3. Rujapa Nanthapodej & Cheng-Hsiang Liu & Krisanarach Nitisiri & Sirorat Pattanapairoj, 2021. "Hybrid Differential Evolution Algorithm and Adaptive Large Neighborhood Search to Solve Parallel Machine Scheduling to Minimize Energy Consumption in Consideration of Machine-Load Balance Problems," Sustainability, MDPI, vol. 13(10), pages 1-25, May.
    4. Donghun Lee & Hyeongwon Kang & Dongjin Lee & Jeonwoo Lee & Kwanho Kim, 2023. "Deep Reinforcement Learning-Based Scheduler on Parallel Dedicated Machine Scheduling Problem towards Minimizing Total Tardiness," Sustainability, MDPI, vol. 15(4), pages 1-14, February.
    5. Tao Dai & Xiangqi Fan, 2021. "Multi-Stove Scheduling for Sustainable On-Demand Food Delivery," Sustainability, MDPI, vol. 13(23), pages 1-13, November.

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