IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i24p6885-d293935.html
   My bibliography  Save this article

Minimizing Makespan in A Two-Machine Flowshop Problem with Processing Time Linearly Dependent on Job Waiting Time

Author

Listed:
  • Dar-Li Yang

    (Department of Information Management, National Formosa University, Yun-Lin 632, Taiwan
    Smart Machine and Intelligent Manufacturing Research Center, National Formosa University, Yun-Lin 632, Taiwan)

  • Wen-Hung Kuo

    (Department of Information Management, National Formosa University, Yun-Lin 632, Taiwan)

Abstract

This paper is aimed at studying a two-machine flowshop scheduling where the processing times are linearly dependent on the waiting times of the jobs prior to processing on the second machine. That is, when a job is processed completely on the first machine, a certain delay time is required before its processing on the second machine. If we would like to reduce the actual waiting time, the processing time of the job on the second machine increases. The objective is to minimize the makespan. When the processing time is reduced, it implies that the consumption of energy is reduced. It is beneficial to environmental sustainability. We show that the proposed problem is NP-hard in the strong sense. A 0-1 mixed integer programming and a heuristic algorithm with computational experiment are proposed. Some cases solved in polynomial time are also provided.

Suggested Citation

  • Dar-Li Yang & Wen-Hung Kuo, 2019. "Minimizing Makespan in A Two-Machine Flowshop Problem with Processing Time Linearly Dependent on Job Waiting Time," Sustainability, MDPI, vol. 11(24), pages 1-18, December.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:24:p:6885-:d:293935
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/24/6885/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/11/24/6885/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mohammad Torkjazi & Nathan Huynh, 2019. "Effectiveness of Dynamic Insertion Scheduling Strategy for Demand-Responsive Paratransit Vehicles Using Agent-Based Simulation," Sustainability, MDPI, vol. 11(19), pages 1-12, September.
    2. Ruiz, Ruben & Stutzle, Thomas, 2008. "An Iterated Greedy heuristic for the sequence dependent setup times flowshop problem with makespan and weighted tardiness objectives," European Journal of Operational Research, Elsevier, vol. 187(3), pages 1143-1159, June.
    3. Yueyue Liu & Xiaoya Liao & Rui Zhang, 2019. "An Enhanced MOPSO Algorithm for Energy-Efficient Single-Machine Production Scheduling," Sustainability, MDPI, vol. 11(19), pages 1-16, September.
    4. Liang Gong & Yinzhen Li & Dejie Xu, 2019. "Combinational Scheduling Model Considering Multiple Vehicle Sizes," Sustainability, MDPI, vol. 11(19), pages 1-14, September.
    5. Oluwatosin Theophilus & Maxim A. Dulebenets & Junayed Pasha & Olumide F. Abioye & Masoud Kavoosi, 2019. "Truck Scheduling at Cross-Docking Terminals: A Follow-Up State-Of-The-Art Review," Sustainability, MDPI, vol. 11(19), pages 1-23, September.
    6. Yunhe Wang & Xiangtao Li & Zhiqiang Ma, 2017. "A Hybrid Local Search Algorithm for the Sequence Dependent Setup Times Flowshop Scheduling Problem with Makespan Criterion," Sustainability, MDPI, vol. 9(12), pages 1-35, December.
    7. Fang-Jye Shiue & Meng-Cong Zheng & Hsin-Yun Lee & Akhmad F.K. Khitam & Pei-Ying Li, 2019. "Renovation Construction Process Scheduling for Long-Term Performance of Buildings: An Application Case of University Campus," Sustainability, MDPI, vol. 11(19), pages 1-19, October.
    8. Shih-Hsin Chen & Yeong-Cheng Liou & Yi-Hui Chen & Kun-Ching Wang, 2019. "Order Acceptance and Scheduling Problem with Carbon Emission Reduction and Electricity Tariffs on a Single Machine," Sustainability, MDPI, vol. 11(19), pages 1-16, September.
    9. S. M. Johnson, 1954. "Optimal two‐ and three‐stage production schedules with setup times included," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 1(1), pages 61-68, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Brammer, Janis & Lutz, Bernhard & Neumann, Dirk, 2022. "Permutation flow shop scheduling with multiple lines and demand plans using reinforcement learning," European Journal of Operational Research, Elsevier, vol. 299(1), pages 75-86.
    2. Liqi Zhang & Lingfa Lu & Shisheng Li, 2016. "New results on two-machine flow-shop scheduling with rejection," Journal of Combinatorial Optimization, Springer, vol. 31(4), pages 1493-1504, May.
    3. Catanzaro, Daniele & Pesenti, Raffaele & Ronco, Roberto, 2023. "Job scheduling under Time-of-Use energy tariffs for sustainable manufacturing: a survey," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1091-1109.
    4. Vincent T’kindt & Federico Della Croce & Mathieu Liedloff, 2022. "Moderate exponential-time algorithms for scheduling problems," 4OR, Springer, vol. 20(4), pages 533-566, December.
    5. Daniel Y. Mo & H. Y. Lam & Weikun Xu & G. T. S. Ho, 2020. "Design of Flexible Vehicle Scheduling Systems for Sustainable Paratransit Services," Sustainability, MDPI, vol. 12(14), pages 1-18, July.
    6. Golpîra, Hêriş, 2020. "Smart Energy-Aware Manufacturing Plant Scheduling under Uncertainty: A Risk-Based Multi-Objective Robust Optimization Approach," Energy, Elsevier, vol. 209(C).
    7. Alexander Grigoriev & Martijn Holthuijsen & Joris van de Klundert, 2005. "Basic scheduling problems with raw material constraints," Naval Research Logistics (NRL), John Wiley & Sons, vol. 52(6), pages 527-535, September.
    8. Pan, Quan-Ke & Gao, Liang & Li, Xin-Yu & Gao, Kai-Zhou, 2017. "Effective metaheuristics for scheduling a hybrid flowshop with sequence-dependent setup times," Applied Mathematics and Computation, Elsevier, vol. 303(C), pages 89-112.
    9. A. G. Leeftink & R. J. Boucherie & E. W. Hans & M. A. M. Verdaasdonk & I. M. H. Vliegen & P. J. Diest, 2018. "Batch scheduling in the histopathology laboratory," Flexible Services and Manufacturing Journal, Springer, vol. 30(1), pages 171-197, June.
    10. Yadong Wang & Baoqiang Fan & Jingang Zhai & Wei Xiong, 2019. "Two-machine flowshop scheduling in a physical examination center," Journal of Combinatorial Optimization, Springer, vol. 37(1), pages 363-374, January.
    11. Shu-Shun Liu & Muhammad Faizal Ardhiansyah Arifin, 2021. "Preventive Maintenance Model for National School Buildings in Indonesia Using a Constraint Programming Approach," Sustainability, MDPI, vol. 13(4), pages 1-24, February.
    12. Miri Gilenson & Dvir Shabtay & Liron Yedidsion & Rohit Malshe, 2021. "Scheduling in multi-scenario environment with an agreeable condition on job processing times," Annals of Operations Research, Springer, vol. 307(1), pages 153-173, December.
    13. Peng-Yeng Yin & Hsin-Min Chen & Yi-Lung Cheng & Ying-Chieh Wei & Ya-Lin Huang & Rong-Fuh Day, 2021. "Minimizing the Makespan in Flowshop Scheduling for Sustainable Rubber Circular Manufacturing," Sustainability, MDPI, vol. 13(5), pages 1-18, February.
    14. 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.
    15. Mansouri, S. Afshin & Aktas, Emel & Besikci, Umut, 2016. "Green scheduling of a two-machine flowshop: Trade-off between makespan and energy consumption," European Journal of Operational Research, Elsevier, vol. 248(3), pages 772-788.
    16. Asoo J. Vakharia & Yih‐Long Chang, 1990. "A simulated annealing approach to scheduling a manufacturing cell," Naval Research Logistics (NRL), John Wiley & Sons, vol. 37(4), pages 559-577, August.
    17. Yu, Tae-Sun & Han, Jun-Hee, 2021. "Scheduling proportionate flow shops with preventive machine maintenance," International Journal of Production Economics, Elsevier, vol. 231(C).
    18. Chargui, Tarik & Ladier, Anne-Laure & Bekrar, Abdelghani & Pan, Shenle & Trentesaux, Damien, 2022. "Towards designing and operating physical internet cross-docks: Problem specifications and research perspectives," Omega, Elsevier, vol. 111(C).
    19. Alfaro-Fernández, Pedro & Ruiz, Rubén & Pagnozzi, Federico & Stützle, Thomas, 2020. "Automatic Algorithm Design for Hybrid Flowshop Scheduling Problems," European Journal of Operational Research, Elsevier, vol. 282(3), pages 835-845.
    20. Hatami, Sara & Ruiz, Rubén & Andrés-Romano, Carlos, 2015. "Heuristics and metaheuristics for the distributed assembly permutation flowshop scheduling problem with sequence dependent setup times," International Journal of Production Economics, Elsevier, vol. 169(C), pages 76-88.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2019:i:24:p:6885-:d:293935. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.