IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i14p1705-d597659.html
   My bibliography  Save this article

A Mathematical Program for Scheduling Preventive Maintenance of Cogeneration Plants with Production

Author

Listed:
  • Khaled Alhamad

    (Laboratory Technology Department, College of Technological Studies, Public Authority for Applied Education and Training, P.O. Box 42325, Shuwaikh 70654, Kuwait
    These authors contributed equally to this work.)

  • Rym M’Hallah

    (Department of Engineering, Faculty of Natural, Mathematical, and Engineering Sciences, King’s College London, Strand S42.1, London WC2R 2ND, UK
    These authors contributed equally to this work.)

  • Cormac Lucas

    (Department of Mathematical Sciences, Brunel University, Uxbridge UB8 3PH, UK
    These authors contributed equally to this work.)

Abstract

This paper considers the scheduling of preventive maintenance for the boilers, turbines, and distillers of power plants that produce electricity and desalinated water. It models the problem as a mathematical program (MP) that maximizes the sum of the minimal ratios of production to the demand of electricity and water during a planning time horizon. This objective encourages the plants’ production and enhances the chances of meeting consumers’ needs. It reduces the chance of power cuts and water shortages that may be caused by emergency disruptions of equipment on the network. To assess its performance and effectiveness, we test the MP on a real system consisting of 32 units and generate a preventive maintenance schedule for a time horizon of 52 weeks (one year). The generated schedule outperforms the schedule established by experts of the water plant; it induces, respectively, 16% and 12% increases in the surpluses while either matching or surpassing the total production. The sensitivity analysis further indicates that the generated schedule can handle unforeseen longer maintenance periods as well as a 120% increase in demand—a sizable realization in a country that heavily relies on electricity to acclimate to the harsh weather conditions. In addition, it suggests the robustness of the schedules with respect to increased demand. In summary, the MP model yields optimal systematic sustainable schedules.

Suggested Citation

  • Khaled Alhamad & Rym M’Hallah & Cormac Lucas, 2021. "A Mathematical Program for Scheduling Preventive Maintenance of Cogeneration Plants with Production," Mathematics, MDPI, vol. 9(14), pages 1-12, July.
  • Handle: RePEc:gam:jmathe:v:9:y:2021:i:14:p:1705-:d:597659
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/14/1705/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/9/14/1705/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Perez-Canto, Salvador & Rubio-Romero, Juan Carlos, 2013. "A model for the preventive maintenance scheduling of power plants including wind farms," Reliability Engineering and System Safety, Elsevier, vol. 119(C), pages 67-75.
    2. B Alidaee & H Wang, 2009. "‘Preventive maintenance scheduling of multi-cogeneration plants using integer programming’," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(9), pages 1295-1297, September.
    3. Canto, Salvador Perez, 2008. "Application of Benders' decomposition to power plant preventive maintenance scheduling," European Journal of Operational Research, Elsevier, vol. 184(2), pages 759-777, January.
    4. Khaled Alhamad & Mohsen Alardhi & Abdulla Almazrouee, 2015. "Preventive Maintenance Scheduling for Multicogeneration Plants with Production Constraints Using Genetic Algorithms," Advances in Operations Research, Hindawi, vol. 2015, pages 1-12, February.
    5. Alex J. Ruiz-Torres & Giuseppe Paletta & Rym M’Hallah, 2017. "Makespan minimisation with sequence-dependent machine deterioration and maintenance events," International Journal of Production Research, Taylor & Francis Journals, vol. 55(2), pages 462-479, January.
    6. Farzaneh Ferdowsi & Hamid Reza Maleki & Sanaz Rivaz, 2020. "Air refueling tanker allocation based on a multi-objective zero-one integer programming model," Operational Research, Springer, vol. 20(4), pages 1913-1938, December.
    7. Nima Safaei & Dragan Banjevic & Andrew Jardine, 2011. "Workforce-constrained maintenance scheduling for military aircraft fleet: a case study," Annals of Operations Research, Springer, vol. 186(1), pages 295-316, June.
    8. M Alardhi & A W Labib, 2008. "Preventive maintenance scheduling of multi-cogeneration plants using integer programming," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(4), pages 503-509, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Khaled Alhamad & Yousuf Alkhezi & M. F. Alhajri, 2022. "Nonlinear Integer Programming for Solving Preventive Maintenance Scheduling Problem for Cogeneration Plants with Production," Sustainability, MDPI, vol. 15(1), pages 1-18, December.
    2. Anatoliy Alabugin & Sergei Aliukov & Tatyana Khudyakova, 2022. "Review of Models for and Socioeconomic Approaches to the Formation of Foresight Control Mechanisms: A Genesis," Sustainability, MDPI, vol. 14(19), pages 1-19, September.

    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. Khaled Alhamad & Yousuf Alkhezi & M. F. Alhajri, 2022. "Nonlinear Integer Programming for Solving Preventive Maintenance Scheduling Problem for Cogeneration Plants with Production," Sustainability, MDPI, vol. 15(1), pages 1-18, December.
    2. Xiang, Yisha, 2013. "Joint optimization of X¯ control chart and preventive maintenance policies: A discrete-time Markov chain approach," European Journal of Operational Research, Elsevier, vol. 229(2), pages 382-390.
    3. Froger, Aurélien & Gendreau, Michel & Mendoza, Jorge E. & Pinson, Éric & Rousseau, Louis-Martin, 2016. "Maintenance scheduling in the electricity industry: A literature review," European Journal of Operational Research, Elsevier, vol. 251(3), pages 695-706.
    4. Petchrompo, Sanyapong & Parlikad, Ajith Kumar, 2019. "A review of asset management literature on multi-asset systems," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 181-201.
    5. Sadeghian, Omid & Mohammadpour Shotorbani, Amin & Mohammadi-Ivatloo, Behnam & Sadiq, Rehan & Hewage, Kasun, 2021. "Risk-averse maintenance scheduling of generation units in combined heat and power systems with demand response," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    6. Changjiu Li & Yong Zhang & Xichao Su & Xinwei Wang, 2022. "An Improved Optimization Algorithm for Aeronautical Maintenance and Repair Task Scheduling Problem," Mathematics, MDPI, vol. 10(20), pages 1-25, October.
    7. Ali Salmasnia & Ali Talesh-Kazemi, 2022. "Integrating inventory planning, pricing and maintenance for perishable products in a two-component parallel manufacturing system with common cause failures," Operational Research, Springer, vol. 22(2), pages 1235-1265, April.
    8. Dilaver, Halit Metehan & Akçay, Alp & van Houtum, Geert-Jan, 2023. "Integrated planning of asset-use and dry-docking for a fleet of maritime assets," International Journal of Production Economics, Elsevier, vol. 256(C).
    9. Shafiee, Mahmood, 2015. "Maintenance logistics organization for offshore wind energy: Current progress and future perspectives," Renewable Energy, Elsevier, vol. 77(C), pages 182-193.
    10. Yonit Barron, 2018. "Group maintenance policies for an R-out-of-N system with phase-type distribution," Annals of Operations Research, Springer, vol. 261(1), pages 79-105, February.
    11. Pavel Y. Gubin & Vladislav P. Oboskalov & Anatolijs Mahnitko & Roman Petrichenko, 2020. "Simulated Annealing, Differential Evolution and Directed Search Methods for Generator Maintenance Scheduling," Energies, MDPI, vol. 13(20), pages 1-26, October.
    12. Yürüşen, Nurseda Y. & Rowley, Paul N. & Watson, Simon J. & Melero, Julio J., 2020. "Automated wind turbine maintenance scheduling," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    13. Xia, Tangbin & Xi, Lifeng & Zhou, Xiaojun & Lee, Jay, 2012. "Dynamic maintenance decision-making for series–parallel manufacturing system based on MAM–MTW methodology," European Journal of Operational Research, Elsevier, vol. 221(1), pages 231-240.
    14. Dahal, Keshav & Al-Arfaj, Khalid & Paudyal, Krishna, 2015. "Modelling generator maintenance scheduling costs in deregulated power markets," European Journal of Operational Research, Elsevier, vol. 240(2), pages 551-561.
    15. Michael D. Teter & Johannes O. Royset & Alexandra M. Newman, 2019. "Modeling uncertainty of expert elicitation for use in risk-based optimization," Annals of Operations Research, Springer, vol. 280(1), pages 189-210, September.
    16. Egging, Ruud, 2013. "Benders Decomposition for multi-stage stochastic mixed complementarity problems – Applied to a global natural gas market model," European Journal of Operational Research, Elsevier, vol. 226(2), pages 341-353.
    17. Marvin L. King & David R. Galbreath & Alexandra M. Newman & Amanda S. Hering, 2020. "Combining regression and mixed-integer programming to model counterinsurgency," Annals of Operations Research, Springer, vol. 292(1), pages 287-320, September.
    18. Rahmaniani, Ragheb & Crainic, Teodor Gabriel & Gendreau, Michel & Rei, Walter, 2017. "The Benders decomposition algorithm: A literature review," European Journal of Operational Research, Elsevier, vol. 259(3), pages 801-817.
    19. Cha, Guesik & Park, Junseok & Moon, Ilkyeong, 2023. "Military aircraft flight and maintenance planning model considering heterogeneous maintenance tasks," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    20. Lin, Boliang & Wu, Jianping & Lin, Ruixi & Wang, Jiaxi & Wang, Hui & Zhang, Xuhui, 2019. "Optimization of high-level preventive maintenance scheduling for high-speed trains," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 261-275.

    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:jmathe:v:9:y:2021:i:14:p:1705-:d:597659. 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.