IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v380y2025ics0306261924024498.html
   My bibliography  Save this article

Approximate and exact approaches to energy-aware job shop scheduling with dynamic energy tariffs and power purchase agreements

Author

Listed:
  • Dunke, Fabian
  • Nickel, Stefan

Abstract

With the goal of strategically analyzing energy tariff structures and operationally establishing energy-aware production schedules, we develop approximate and exact methods for generating energy-efficient Pareto schedules in job shop environments. Energy costs are influenced by dynamic market prices and available fixed tariff contracts. In this context, power purchase agreements (PPAs) have recently emerged to support renewable energy generation, guaranteeing a fixed price for a specified amount of renewable energy sold to industrial customers. To integrate energy-related aspects, we extend the job shop setting by machine states, energy sources, carbon emissions, energy consumption, and time-dynamic energy tariffs. We develop a time-indexed mathematical programming formulation integrated into the ϵ-constraint method to achieve Pareto efficiency concerning production makespan and energy costs minimization. Our research addresses the challenges of integrating energy market characteristics with production scheduling, tackling nonlinearity and time dynamics while managing NP-hardness of energy-aware job shops. Key contributions include developing a model-based methodology for optimizing energy-aware schedules, integrating this approach within an algorithmic framework for determining Pareto schedules, and creating a decision-making workflow for analyzing energy tariffs. In particular, this facilitates an analysis of the largely unexplored PPA tariff. Using 2023 energy price data from the European Network of Transmission System Operators for Electricity (ENTSO-E), we provide extensive numerical experimentation to analyze trade-offs related to schedule determination, energy price data, PPA and tariff specifications, and working time restrictions. This provides insights into the interplay between tariff selection and production scheduling, relevant to the strategic financing and operational management of energy-aware production systems.

Suggested Citation

  • Dunke, Fabian & Nickel, Stefan, 2025. "Approximate and exact approaches to energy-aware job shop scheduling with dynamic energy tariffs and power purchase agreements," Applied Energy, Elsevier, vol. 380(C).
    • Handle: RePEc:eee:appene:v:380:y:2025:i:c:s0306261924024498
    DOI: 10.1016/j.apenergy.2024.125065
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261924024498
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2024.125065?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. João M. R. C. Fernandes & Seyed Mahdi Homayouni & Dalila B. M. M. Fontes, 2022. "Energy-Efficient Scheduling in Job Shop Manufacturing Systems: A Literature Review," Sustainability, MDPI, vol. 14(10), pages 1-34, May.
    2. MohammadMohsen Aghelinejad & Yassine Ouazene & Alice Yalaoui, 2018. "Production scheduling optimisation with machine state and time-dependent energy costs," International Journal of Production Research, Taylor & Francis Journals, vol. 56(16), pages 5558-5575, August.
    3. Sven Schulz & Udo Buscher & Liji Shen, 2020. "Multi-objective hybrid flow shop scheduling with variable discrete production speed levels and time-of-use energy prices," Journal of Business Economics, Springer, vol. 90(9), pages 1315-1343, November.
    4. Kaifeng Geng & Chunming Ye & Zhen hua Dai & Li Liu, 2020. "Bi-Objective Re-Entrant Hybrid Flow Shop Scheduling considering Energy Consumption Cost under Time-of-Use Electricity Tariffs," Complexity, Hindawi, vol. 2020, pages 1-17, February.
    5. 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.
    6. Cheng-Hsiang Liu, 2016. "Mathematical programming formulations for single-machine scheduling problems while considering renewable energy uncertainty," International Journal of Production Research, Taylor & Francis Journals, vol. 54(4), pages 1122-1133, February.
    7. Zhai, Y. & Biel, K. & Zhao, F. & Sutherland, J. W., 2017. "Dynamic scheduling of a flow shop with on-site wind generation for energy cost reduction under real time electricity pricing," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 87366, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    8. Li, Yuxin & Wang, Jiangjiang & Zhou, Yuan & Wei, Changqi & Guan, Zhimin & Chen, Haiyue, 2023. "Multi-dimension day-ahead scheduling optimization of a community-scale solar-driven CCHP system with demand-side management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 185(C).
    9. Gert Peersman & Joris Wauters, 2022. "Heterogeneous household responses to energy price shocks," Working Paper Research 416, National Bank of Belgium.
    10. Biel, K. & Glock, C. H., 2016. "Systematic literature review of decision support models for energy-efficient production planning," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 83071, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    11. Dütschke, Elisabeth & Paetz, Alexandra-Gwyn, 2013. "Dynamic electricity pricing—Which programs do consumers prefer?," Energy Policy, Elsevier, vol. 59(C), pages 226-234.
    12. 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.
    13. Ruilin Pan & Qiong Wang & Jianhua Cao & Chunliu Zhou, 2024. "Deep reinforcement learning for solving steelmaking-continuous casting scheduling problems under time-of-use tariffs," International Journal of Production Research, Taylor & Francis Journals, vol. 62(1-2), pages 404-420, January.
    14. Matthias Gerhard Wichmann & Christoph Johannes & Thomas Stefan Spengler, 2019. "An extension of the general lot-sizing and scheduling problem (GLSP) with time-dependent energy prices," Journal of Business Economics, Springer, vol. 89(5), pages 481-514, July.
    15. Park, Myoung-Ju & Ham, Andy, 2022. "Energy-aware flexible job shop scheduling under time-of-use pricing," International Journal of Production Economics, Elsevier, vol. 248(C).
    16. Peersman, Gert & Wauters, Joris, 2024. "Heterogeneous household responses to energy price shocks," Energy Economics, Elsevier, vol. 132(C).
    17. Sebastian Busch & Ruben Kasdorp & Derck Koolen & Arnaud Mercier & Magdalena Spooner, 2023. "The Development of Renewable Energy in the Electricity Market," European Economy - Discussion Papers 187, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    18. Ghorbanzadeh, Masoumeh & Ranjbar, Mohammad, 2023. "Energy-aware production scheduling in the flow shop environment under sequence-dependent setup times, group scheduling and renewable energy constraints," European Journal of Operational Research, Elsevier, vol. 307(2), pages 519-537.
    19. Konstantin Biel & Fu Zhao & John W. Sutherland & Christoph H. Glock, 2018. "Flow shop scheduling with grid-integrated onsite wind power using stochastic MILP," International Journal of Production Research, Taylor & Francis Journals, vol. 56(5), pages 2076-2098, March.
    20. Shijin Wang & Zhanguo Zhu & Kan Fang & Feng Chu & Chengbin Chu, 2018. "Scheduling on a two-machine permutation flow shop under time-of-use electricity tariffs," International Journal of Production Research, Taylor & Francis Journals, vol. 56(9), pages 3173-3187, May.
    21. Markus Hilbert & Andreas Dellnitz & Andreas Kleine, 2023. "Production planning under RTP, TOU and PPA considering a redox flow battery storage system," Annals of Operations Research, Springer, vol. 328(2), pages 1409-1436, September.
    22. Hyun-Jung Kim & Eun-Seok Kim & Jun-Ho Lee & Lixin Tang & Yang Yang, 2022. "Single-machine scheduling with energy generation and storage systems," International Journal of Production Research, Taylor & Francis Journals, vol. 60(23), pages 7033-7052, December.
    23. Anghinolfi, Davide & Paolucci, Massimo & Ronco, Roberto, 2021. "A bi-objective heuristic approach for green identical parallel machine scheduling," European Journal of Operational Research, Elsevier, vol. 289(2), pages 416-434.
    24. Shen, Liji & Dauzère-Pérès, Stéphane & Maecker, Söhnke, 2023. "Energy cost efficient scheduling in flexible job-shop manufacturing systems," European Journal of Operational Research, Elsevier, vol. 310(3), pages 992-1016.
    25. Heydar, Mojtaba & Mardaneh, Elham & Loxton, Ryan, 2022. "Approximate dynamic programming for an energy-efficient parallel machine scheduling problem," European Journal of Operational Research, Elsevier, vol. 302(1), pages 363-380.
    26. Biel, K. & Zhao, F. & Sutherland, J. & Glock, C. H., 2018. "Flow shop scheduling with grid-integrated onsite wind power using stochastic MILP," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 88879, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    27. Xin Feng & Hongjun Peng, 2024. "Robust identical parallel machine scheduling with two-stage time-of-use tariff and not-all-machine option," International Journal of Production Research, Taylor & Francis Journals, vol. 62(1-2), pages 380-403, January.
    28. Eid, Cherrelle & Koliou, Elta & Valles, Mercedes & Reneses, Javier & Hakvoort, Rudi, 2016. "Time-based pricing and electricity demand response: Existing barriers and next steps," Utilities Policy, Elsevier, vol. 40(C), pages 15-25.
    29. Goutam Dutta & Krishnendranath Mitra, 2017. "A literature review on dynamic pricing of electricity," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(10), pages 1131-1145, October.
    30. Hongliang Zhang & Yujuan Wu & Ruilin Pan & Gongjie Xu, 2021. "Two-stage parallel speed-scaling machine scheduling under time-of-use tariffs," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 91-112, January.
    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. Markus Hilbert & Andreas Kleine & Andreas Dellnitz, 2024. "Towards the concept of gas-to-power demand response," Journal of Business Economics, Springer, vol. 94(1), pages 113-135, January.
    2. 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.
    3. 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.
    4. Ghorbanzadeh, Masoumeh & Ranjbar, Mohammad, 2023. "Energy-aware production scheduling in the flow shop environment under sequence-dependent setup times, group scheduling and renewable energy constraints," European Journal of Operational Research, Elsevier, vol. 307(2), pages 519-537.
    5. Markus Hilbert & Andreas Dellnitz & Andreas Kleine, 2023. "Production planning under RTP, TOU and PPA considering a redox flow battery storage system," Annals of Operations Research, Springer, vol. 328(2), pages 1409-1436, September.
    6. 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).
    7. Shen, Liji & Dauzère-Pérès, Stéphane & Maecker, Söhnke, 2023. "Energy cost efficient scheduling in flexible job-shop manufacturing systems," European Journal of Operational Research, Elsevier, vol. 310(3), pages 992-1016.
    8. Golpîra, Hêriş, 2020. "Smart Energy-Aware Manufacturing Plant Scheduling under Uncertainty: A Risk-Based Multi-Objective Robust Optimization Approach," Energy, Elsevier, vol. 209(C).
    9. Patrick Ludwig & Christian Winzer, 2022. "Tariff Menus to Avoid Rebound Peaks: Results from a Discrete Choice Experiment with Swiss Customers," Energies, MDPI, vol. 15(17), pages 1-21, August.
    10. Massimo Bertolini & Francesco Leali & Davide Mezzogori & Cristina Renzi, 2023. "A Keyword, Taxonomy and Cartographic Research Review of Sustainability Concepts for Production Scheduling in Manufacturing Systems," Sustainability, MDPI, vol. 15(8), pages 1-21, April.
    11. 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.
    12. Balwin Bokor & Klaus Altendorfer & Andrea Matta, 2025. "Optimizing Energy Consumption in Stochastic Production Systems: Using a Simulation-Based Approach for Stopping Policy," Papers 2505.11536, arXiv.org.
    13. Tan, Barış & Karabağ, Oktay & Khayyati, Siamak, 2023. "Production and energy mode control of a production-inventory system," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1176-1187.
    14. Park, Myoung-Ju & Ham, Andy, 2022. "Energy-aware flexible job shop scheduling under time-of-use pricing," International Journal of Production Economics, Elsevier, vol. 248(C).
    15. Andreas Dellnitz & Damian Braschczok & Jonas Ostmeyer & Markus Hilbert & Andreas Kleine, 2020. "Energy costs vs. carbon dioxide emissions in short-term production planning," Journal of Business Economics, Springer, vol. 90(9), pages 1383-1407, November.
    16. Suzanne, Elodie & Absi, Nabil & Borodin, Valeria, 2020. "Towards circular economy in production planning: Challenges and opportunities," European Journal of Operational Research, Elsevier, vol. 287(1), pages 168-190.
    17. Shun Jia & Yang Yang & Shuyu Li & Shang Wang & Anbang Li & Wei Cai & Yang Liu & Jian Hao & Luoke Hu, 2024. "The Green Flexible Job-Shop Scheduling Problem Considering Cost, Carbon Emissions, and Customer Satisfaction under Time-of-Use Electricity Pricing," Sustainability, MDPI, vol. 16(6), pages 1-22, March.
    18. Wichmann, Matthias Gerhard & Johannes, Christoph & Spengler, Thomas Stefan, 2019. "Energy-oriented Lot-Sizing and Scheduling considering energy storages," International Journal of Production Economics, Elsevier, vol. 216(C), pages 204-214.
    19. Krishnendranath Mitra & Goutam Dutta, 2021. "A novel method of market segmentation and market study for dynamic pricing of retail electricity in India: an experimental approach in a university setup," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(2), pages 162-184, April.
    20. Hasani, Ali & Hosseini, Seyed Mohammad Hassan, 2020. "A bi-objective flexible flow shop scheduling problem with machine-dependent processing stages: Trade-off between production costs and energy consumption," Applied Mathematics and Computation, Elsevier, vol. 386(C).

    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:eee:appene:v:380:y:2025:i:c:s0306261924024498. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.