IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i21p7446-d674703.html
   My bibliography  Save this article

Analysis of Energy Efficient Scheduling of the Manufacturing Line with Finite Buffer Capacity and Machine Setup and Shutdown Times

Author

Listed:
  • Adrian Kampa

    (Department of Engineering Processes Automation and Integrated Manufacturing Systems, Silesian University of Technology, Konarskiego 18A, 44-100 Gliwice, Poland)

  • Iwona Paprocka

    (Department of Engineering Processes Automation and Integrated Manufacturing Systems, Silesian University of Technology, Konarskiego 18A, 44-100 Gliwice, Poland)

Abstract

The aim of this paper is to present a model of energy efficient scheduling for series production systems during operation, including setup and shutdown activities. The flow shop system together with setup, shutdown times and energy consumption are considered. Production tasks enter the system with exponentially distributed interarrival times and are carried out according to the times assumed as predefined. Tasks arriving from one waiting queue are handled in the order set by the Multi Objective Immune Algorithm. Tasks are stored in a finite-capacity buffer if machines are busy, or setup activities are being performed. Whenever a production system is idle, machines are stopped according to shutdown times in order to save energy. A machine requires setup time before executing the first batch of jobs after the idle time. Scientists agree that turning off an idle machine is a common measure that is appropriate for all types of workshops, but usually requires more steps, such as setup and shutdown. Literature analysis shows that there is a research gap regarding multi-objective algorithms, as minimizing energy consumption is not the only factor affecting the total manufacturing cost—there are other factors, such as late delivery cost or early delivery cost with additional storage cost, which make the optimization of the total cost of the production process more complicated. Another goal is to develop previous scheduling algorithms and research framework for energy efficient scheduling. The impact of the input data on the production system performance and energy consumption for series production is investigated in serial, parallel or serial–parallel flows. Parallel flow of upcoming tasks achieves minimum values of makespan criterion. Serial and serial–parallel flows of arriving tasks ensure minimum cost of energy consumption. Parallel flow of arriving tasks ensures minimum values of the costs of tardiness or premature execution. Parallel flow or serial–parallel flow of incoming tasks allows one to implement schedules with tasks that are not delayed.

Suggested Citation

  • Adrian Kampa & Iwona Paprocka, 2021. "Analysis of Energy Efficient Scheduling of the Manufacturing Line with Finite Buffer Capacity and Machine Setup and Shutdown Times," Energies, MDPI, vol. 14(21), pages 1-25, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:7446-:d:674703
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/21/7446/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/21/7446/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Vangelis Marinakis & Themistoklis Koutsellis & Alexandros Nikas & Haris Doukas, 2021. "AI and Data Democratisation for Intelligent Energy Management," Energies, MDPI, vol. 14(14), pages 1-14, July.
    2. Lin Herenčić & Perica Ilak & Ivan Rajšl, 2019. "Effects of Local Electricity Trading on Power Flows and Voltage Levels for Different Elasticities and Prices," Energies, MDPI, vol. 12(24), pages 1-19, December.
    3. Lucian Mihet-Popa & Sergio Saponara, 2021. "Power Converters, Electric Drives and Energy Storage Systems for Electrified Transportation and Smart Grid Applications," Energies, MDPI, vol. 14(14), pages 1-5, July.
    4. Xiuli Wu & Xianli Shen & Qi Cui, 2018. "Multi-Objective Flexible Flow Shop Scheduling Problem Considering Variable Processing Time due to Renewable Energy," Sustainability, MDPI, vol. 10(3), pages 1-30, March.
    5. Koo-Hyung Chung & Don Hur, 2020. "Towards the Design of P2P Energy Trading Scheme Based on Optimal Energy Scheduling for Prosumers," Energies, MDPI, vol. 13(19), pages 1-15, October.
    6. Arroyo, Jose Elias Claudio & Armentano, Vinicius Amaral, 2005. "Genetic local search for multi-objective flowshop scheduling problems," European Journal of Operational Research, Elsevier, vol. 167(3), pages 717-738, December.
    7. Gahm, Christian & Denz, Florian & Dirr, Martin & Tuma, Axel, 2016. "Energy-efficient scheduling in manufacturing companies: A review and research framework," European Journal of Operational Research, Elsevier, vol. 248(3), pages 744-757.
    8. Vandana & S. R. Singh & Dharmendra Yadav & Biswajit Sarkar & Mitali Sarkar, 2021. "Impact of Energy and Carbon Emission of a Supply Chain Management with Two-Level Trade-Credit Policy," Energies, MDPI, vol. 14(6), pages 1-19, March.
    9. Andre Dionisio Rocha & Nelson Freitas & Duarte Alemão & Magno Guedes & Renato Martins & José Barata, 2021. "Event-Driven Interoperable Manufacturing Ecosystem for Energy Consumption Monitoring," Energies, MDPI, vol. 14(12), pages 1-19, June.
    10. Iwona Paprocka, 2018. "Evaluation of the Effects of a Machine Failure on the Robustness of a Job Shop System—Proactive Approaches," Sustainability, MDPI, vol. 11(1), pages 1-18, December.
    11. Liaw, Ching-Fang, 2000. "A hybrid genetic algorithm for the open shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 124(1), pages 28-42, July.
    12. Weiwei Cui & Lin Li & Zhiqiang Lu, 2019. "Energy‐efficient scheduling for sustainable manufacturing systems with renewable energy resources," Naval Research Logistics (NRL), John Wiley & Sons, vol. 66(2), pages 154-173, March.
    13. Maroua Nouiri & Damien Trentesaux & Abdelghani Bekrar, 2019. "Towards Energy Efficient Scheduling of Manufacturing Systems through Collaboration between Cyber Physical Production and Energy Systems," Energies, MDPI, vol. 12(23), pages 1-30, November.
    14. Zhou, Shengchao & Jin, Mingzhou & Du, Ni, 2020. "Energy-efficient scheduling of a single batch processing machine with dynamic job arrival times," Energy, Elsevier, vol. 209(C).
    15. Maryam Mousavi & Hwa Jen Yap & Siti Nurmaya Musa & Farzad Tahriri & Siti Zawiah Md Dawal, 2017. "Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-24, March.
    16. Muhammad Fahad & Arsalan Shahid & Ravi Reddy Manumachu & Alexey Lastovetsky, 2019. "A Comparative Study of Methods for Measurement of Energy of Computing," Energies, MDPI, vol. 12(11), pages 1-42, June.
    17. Lvjiang Yin & Xinyu Li & Chao Lu & Liang Gao, 2016. "Energy-Efficient Scheduling Problem Using an Effective Hybrid Multi-Objective Evolutionary Algorithm," Sustainability, MDPI, vol. 8(12), pages 1-33, December.
    18. Qingquan Jiang & Xiaoya Liao & Rui Zhang & Qiaozhen Lin, 2020. "Energy-Saving Production Scheduling in a Single-Machine Manufacturing System by Improved Particle Swarm Optimization," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-16, November.
    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. Wojciech M. Kempa & Dariusz Kurzyk, 2022. "Analysis of Non-Steady Queue-Length Distribution in a Finite-Buffer Model with Group Arrivals and Power Saving Mechanism with Setups," Energies, MDPI, vol. 15(22), pages 1-15, November.

    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. Hajo Terbrack & Thorsten Claus & Frank Herrmann, 2021. "Energy-Oriented Production Planning in Industry: A Systematic Literature Review and Classification Scheme," Sustainability, MDPI, vol. 13(23), pages 1-32, December.
    2. Andrzej Bożek, 2020. "Energy Cost-Efficient Task Positioning in Manufacturing Systems," Energies, MDPI, vol. 13(19), pages 1-21, September.
    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. Gaggero, Mauro & Paolucci, Massimo & Ronco, Roberto, 2023. "Exact and heuristic solution approaches for energy-efficient identical parallel machine scheduling with time-of-use costs," European Journal of Operational Research, Elsevier, vol. 311(3), pages 845-866.
    5. Xiangxin An & Guojin Si & Tangbin Xia & Qinming Liu & Yaping Li & Rui Miao, 2022. "Operation and Maintenance Optimization for Manufacturing Systems with Energy Management," Energies, MDPI, vol. 15(19), pages 1-19, October.
    6. Azim, M. Imran & Tushar, Wayes & Saha, Tapan K. & Yuen, Chau & Smith, David, 2022. "Peer-to-peer kilowatt and negawatt trading: A review of challenges and recent advances in distribution networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 169(C).
    7. Catanzaro, Daniele & Pesenti, Raffaele & Ronco, Roberto, 2021. "Job Scheduling under Time-of-Use Energy Tariffs for Sustainable Manufacturing: A Survey," LIDAM Discussion Papers CORE 2021019, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    8. Ławrynowicz Anna, 2011. "Genetic Algorithms for Solving Scheduling Problems in Manufacturing Systems," Foundations of Management, Sciendo, vol. 3(2), pages 7-26, January.
    9. Liaqat Ali & M. Imran Azim & Nabin B. Ojha & Jan Peters & Vivek Bhandari & Anand Menon & Vinod Tiwari & Jemma Green & S.M. Muyeen, 2023. "Balancing Usage Profiles and Benefitting End Users through Blockchain Based Local Energy Trading: A German Case Study," Energies, MDPI, vol. 16(17), pages 1-18, August.
    10. Abbas Hamze & Yassine Ouazene & Nazir Chebbo & Imane Maatouk, 2019. "Multisources of Energy Contracting Strategy with an Ecofriendly Factor and Demand Uncertainties," Energies, MDPI, vol. 12(20), pages 1-24, October.
    11. Shahaboddin Shamshirband & Mohammad Shojafar & A. Hosseinabadi & Maryam Kardgar & M. Nasir & Rodina Ahmad, 2015. "OSGA: genetic-based open-shop scheduling with consideration of machine maintenance in small and medium enterprises," Annals of Operations Research, Springer, vol. 229(1), pages 743-758, June.
    12. Eid Gul & Giorgio Baldinelli & Pietro Bartocci, 2022. "Energy Transition: Renewable Energy-Based Combined Heat and Power Optimization Model for Distributed Communities," Energies, MDPI, vol. 15(18), pages 1-18, September.
    13. Xiaoyan Li & Xuedong Liang & Zhi Li, 2023. "The Strategy of Strengthening Efficiency and Environmental Performance of Product Changeover in the Multiproduct Production System," SAGE Open, , vol. 13(3), pages 21582440231, September.
    14. Ayman A. Aly & Bassem F. Felemban & Ardashir Mohammadzadeh & Oscar Castillo & Andrzej Bartoszewicz, 2021. "Frequency Regulation System: A Deep Learning Identification, Type-3 Fuzzy Control and LMI Stability Analysis," Energies, MDPI, vol. 14(22), pages 1-21, November.
    15. Fernando, Yudi & Hor, Wei Lin, 2017. "Impacts of energy management practices on energy efficiency and carbon emissions reduction: A survey of malaysian manufacturing firms," Resources, Conservation & Recycling, Elsevier, vol. 126(C), pages 62-73.
    16. Naderi, B. & Zandieh, M., 2014. "Modeling and scheduling no-wait open shop problems," International Journal of Production Economics, Elsevier, vol. 158(C), pages 256-266.
    17. Kumar, Akhilesh & Prakash & Tiwari, M.K. & Shankar, Ravi & Baveja, Alok, 2006. "Solving machine-loading problem of a flexible manufacturing system with constraint-based genetic algorithm," European Journal of Operational Research, Elsevier, vol. 175(2), pages 1043-1069, December.
    18. Gerardo Minella & Rubén Ruiz & Michele Ciavotta, 2008. "A Review and Evaluation of Multiobjective Algorithms for the Flowshop Scheduling Problem," INFORMS Journal on Computing, INFORMS, vol. 20(3), pages 451-471, August.
    19. Pempera, Jaroslaw & Smutnicki, Czeslaw, 2018. "Open shop cyclic scheduling," European Journal of Operational Research, Elsevier, vol. 269(2), pages 773-781.
    20. Rozmysław Mieński & Przemysław Urbanek & Irena Wasiak, 2021. "Using Energy Storage Inverters of Prosumer Installations for Voltage Control in Low-Voltage Distribution Networks," Energies, MDPI, vol. 14(4), pages 1-21, February.

    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:jeners:v:14:y:2021:i:21:p:7446-:d:674703. 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.