IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v174y2016icp88-100.html
   My bibliography  Save this article

Energy-Performance as a driver for optimal production planning

Author

Listed:
  • Salahi, Niloofar
  • Jafari, Mohsen A.

Abstract

In this paper, we present energy-aware production planning using a two-dimensional “Energy-Performance” measure. With this measure, the production plan explicitly takes into account machine-level requirements, process control strategies, product types and demand patterns. The “Energy-Performance” measure is developed based on an existing concept, namely, “Specific Energy” at machine level. It is further expanded to an “Energy-Performance” profile for a production line. A production planning problem is formulated as a stochastic MILP with risk-averse constraints to account for manufacturer’s risk averseness. The objective is to attain an optimal production plan that minimizes the total loss distribution subject to system throughput targets, probabilistic risk constraints and constraints imposed by the underlying “Energy-Performance” pattern. Electricity price and demand per unit time are assumed to be stochastic. Conditional Value at Risk (CVaR) of loss distributions is used as the manufacturer’s risk measure. Both single-machine and production lines are studied for different profiles and electricity pricing schemes. It is shown that the shape of “Energy-Performance” profile can change optimal plans.

Suggested Citation

  • Salahi, Niloofar & Jafari, Mohsen A., 2016. "Energy-Performance as a driver for optimal production planning," Applied Energy, Elsevier, vol. 174(C), pages 88-100.
    • Handle: RePEc:eee:appene:v:174:y:2016:i:c:p:88-100
    DOI: 10.1016/j.apenergy.2016.04.085
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261916305505
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2016.04.085?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. Kelly Kissock, J. & Eger, Carl, 2008. "Measuring industrial energy savings," Applied Energy, Elsevier, vol. 85(5), pages 347-361, May.
    2. Wang, Yong & Li, Lin, 2014. "Time-of-use based electricity cost of manufacturing systems: Modeling and monotonicity analysis," International Journal of Production Economics, Elsevier, vol. 156(C), pages 246-259.
    3. Thollander, Patrik & Backlund, Sandra & Trianni, Andrea & Cagno, Enrico, 2013. "Beyond barriers – A case study on driving forces for improved energy efficiency in the foundry industries in Finland, France, Germany, Italy, Poland, Spain, and Sweden," Applied Energy, Elsevier, vol. 111(C), pages 636-643.
    4. Pratham, 2015. "Annual Status of Education Report (Rural) 2014," Working Papers id:6398, eSocialSciences.
    5. Cagno, Enrico & Trianni, Andrea, 2013. "Exploring drivers for energy efficiency within small- and medium-sized enterprises: First evidences from Italian manufacturing enterprises," Applied Energy, Elsevier, vol. 104(C), pages 276-285.
    6. Xu, Xianshuo & Zhao, Tao & Liu, Nan & Kang, Jidong, 2014. "Changes of energy-related GHG emissions in China: An empirical analysis from sectoral perspective," Applied Energy, Elsevier, vol. 132(C), pages 298-307.
    7. Palm, Jenny & Thollander, Patrik, 2010. "An interdisciplinary perspective on industrial energy efficiency," Applied Energy, Elsevier, vol. 87(10), pages 3255-3261, October.
    8. May, Gökan & Barletta, Ilaria & Stahl, Bojan & Taisch, Marco, 2015. "Energy management in production: A novel method to develop key performance indicators for improving energy efficiency," Applied Energy, Elsevier, vol. 149(C), pages 46-61.
    9. Khayyam, Hamid & Naebe, Minoo & Bab-Hadiashar, Alireza & Jamshidi, Farshid & Li, Quanxiang & Atkiss, Stephen & Buckmaster, Derek & Fox, Bronwyn, 2015. "Stochastic optimization models for energy management in carbonization process of carbon fiber production," Applied Energy, Elsevier, vol. 158(C), pages 643-655.
    10. Ahmed, Shabbir & Cakmak, Ulas & Shapiro, Alexander, 2007. "Coherent risk measures in inventory problems," European Journal of Operational Research, Elsevier, vol. 182(1), pages 226-238, October.
    11. World Economic Forum & World Bank & African Development Bank & Organisation for Economic Co-operation and Development, 2015. "The Africa Competitiveness Report 2015," World Bank Publications - Books, The World Bank Group, number 22014.
    12. Karali, Nihan & Xu, Tengfang & Sathaye, Jayant, 2014. "Reducing energy consumption and CO2 emissions by energy efficiency measures and international trading: A bottom-up modeling for the U.S. iron and steel sector," Applied Energy, Elsevier, vol. 120(C), pages 133-146.
    13. Zhang, Dali & Xu, Huifu & Wu, Yue, 2009. "Single and multi-period optimal inventory control models with risk-averse constraints," European Journal of Operational Research, Elsevier, vol. 199(2), pages 420-434, December.
    14. Kong, Lingbo & Price, Lynn & Hasanbeigi, Ali & Liu, Huanbin & Li, Jigeng, 2013. "Potential for reducing paper mill energy use and carbon dioxide emissions through plant-wide energy audits: A case study in China," Applied Energy, Elsevier, vol. 102(C), pages 1334-1342.
    15. Ferreira, R.S. & Barroso, L.A. & Carvalho, M.M., 2012. "Demand response models with correlated price data: A robust optimization approach," Applied Energy, Elsevier, vol. 96(C), pages 133-149.
    16. Saidur, R. & Mekhilef, S., 2010. "Energy use, energy savings and emission analysis in the Malaysian rubber producing industries," Applied Energy, Elsevier, vol. 87(8), pages 2746-2758, August.
    17. Unknown, 2015. "JARE Editors' Report: Back Matter," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 40(3), pages 1-7.
    18. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
    19. Unknown, 2015. "Editor's Report," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 40(1), pages 1-1.
    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. Ghanbarzadeh-Shams, M. & Ghasemy Yaghin, R. & Sadeghi, A.H., 2022. "A hybrid fuzzy multi-objective model for carpet production planning with reverse logistics under uncertainty," Socio-Economic Planning Sciences, Elsevier, vol. 83(C).
    2. Perroni, Marcos G. & Gouvea da Costa, Sergio E. & Pinheiro de Lima, Edson & Vieira da Silva, Wesley & Tortato, Ubiratã, 2018. "Measuring energy performance: A process based approach," Applied Energy, Elsevier, vol. 222(C), pages 540-553.
    3. Zhaohui Feng & Xinru Ding & Hua Zhang & Ying Liu & Wei Yan & Xiaoli Jiang, 2023. "An Energy Consumption Estimation Method for the Tool Setting Process in CNC Milling Based on the Modular Arrangement of Predetermined Time Standards," Energies, MDPI, vol. 16(20), pages 1-18, October.
    4. Tomas Macak & Jan Hron & Jaromir Stusek, 2020. "A Causal Model of the Sustainable Use of Resources: A Case Study on a Woodworking Process," Sustainability, MDPI, vol. 12(21), pages 1-22, October.
    5. Shun Jia & Qingwen Yuan & Wei Cai & Qinghe Yuan & Conghu Liu & Jingxiang Lv & Zhongwei Zhang, 2018. "Establishment of an Improved Material-Drilling Power Model to Support Energy Management of Drilling Processes," Energies, MDPI, vol. 11(8), pages 1-16, August.
    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. Luo, Chao & Ju, Yanbing & Santibanez Gonzalez, Ernesto D.R. & Dong, Peiwu & Wang, Aihua, 2020. "The waste-to-energy incineration plant site selection based on hesitant fuzzy linguistic Best-Worst method ANP and double parameters TOPSIS approach: A case study in China," Energy, Elsevier, vol. 211(C).
    8. Jia, Shun & Cai, Wei & Liu, Conghu & Zhang, Zhongwei & Bai, Shuowei & Wang, Qiuyan & Li, Shuoshuo & Hu, Luoke, 2021. "Energy modeling and visualization analysis method of drilling processes in the manufacturing industry," Energy, Elsevier, vol. 228(C).
    9. Kiki Ayu & Akilu Yunusa-Kaltungo, 2020. "A Holistic Framework for Supporting Maintenance and Asset Management Life Cycle Decisions for Power Systems," Energies, MDPI, vol. 13(8), pages 1-32, April.

    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. May, Gökan & Barletta, Ilaria & Stahl, Bojan & Taisch, Marco, 2015. "Energy management in production: A novel method to develop key performance indicators for improving energy efficiency," Applied Energy, Elsevier, vol. 149(C), pages 46-61.
    2. Hasan, A S M Monjurul & Tuhin, Rashedul Amin & Ullah, Mahfuz & Sakib, Taiyeb Hasan & Thollander, Patrik & Trianni, Andrea, 2021. "A comprehensive investigation of energy management practices within energy intensive industries in Bangladesh," Energy, Elsevier, vol. 232(C).
    3. Riitta-Liisa Arpiainen & Päivi Tynjälä, 2017. "Introducing Team Learning in a Developing Economy: Students’ Experiences of Experiential Entrepreneurship Education in Namibia," Journal of Enterprising Culture (JEC), World Scientific Publishing Co. Pte. Ltd., vol. 25(02), pages 179-210, June.
    4. Perroni, Marcos G. & Gouvea da Costa, Sergio E. & Pinheiro de Lima, Edson & Vieira da Silva, Wesley & Tortato, Ubiratã, 2018. "Measuring energy performance: A process based approach," Applied Energy, Elsevier, vol. 222(C), pages 540-553.
    5. Solnørdal, Mette Talseth & Thyholdt, Sverre Braathen, 2019. "Absorptive capacity and energy efficiency in manufacturing firms – An empirical analysis in Norway," Energy Policy, Elsevier, vol. 132(C), pages 978-990.
    6. Werner König & Sabine Löbbe & Stefan Büttner & Christian Schneider, 2020. "Establishing Energy Efficiency—Drivers for Energy Efficiency in German Manufacturing Small- and Medium-Sized Enterprises," Energies, MDPI, vol. 13(19), pages 1-31, October.
    7. Mette Talseth Solnørdal & Lene Foss, 2018. "Closing the Energy Efficiency Gap—A Systematic Review of Empirical Articles on Drivers to Energy Efficiency in Manufacturing Firms," Energies, MDPI, vol. 11(3), pages 1-30, February.
    8. May, Gökan & Stahl, Bojan & Taisch, Marco, 2016. "Energy management in manufacturing: Toward eco-factories of the future – A focus group study," Applied Energy, Elsevier, vol. 164(C), pages 628-638.
    9. Du, Huibin & Matisoff, Daniel C. & Wang, Yangyang & Liu, Xi, 2016. "Understanding drivers of energy efficiency changes in China," Applied Energy, Elsevier, vol. 184(C), pages 1196-1206.
    10. A. S. M. Monjurul Hasan & Rakib Hossain & Rashedul Amin Tuhin & Taiyeb Hasan Sakib & Patrik Thollander, 2019. "Empirical Investigation of Barriers and Driving Forces for Efficient Energy Management Practices in Non-Energy-Intensive Manufacturing Industries of Bangladesh," Sustainability, MDPI, vol. 11(9), pages 1-13, May.
    11. Thollander, Patrik & Backlund, Sandra & Trianni, Andrea & Cagno, Enrico, 2013. "Beyond barriers – A case study on driving forces for improved energy efficiency in the foundry industries in Finland, France, Germany, Italy, Poland, Spain, and Sweden," Applied Energy, Elsevier, vol. 111(C), pages 636-643.
    12. Natascha Helena Franz Hoppen & Samile Andréa de Souza Vanz, 2016. "Neurosciences in Brazil: a bibliometric study of main characteristics, collaboration and citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(1), pages 121-141, October.
    13. Qiu, Ruozhen & Shang, Jennifer & Huang, Xiaoyuan, 2014. "Robust inventory decision under distribution uncertainty: A CVaR-based optimization approach," International Journal of Production Economics, Elsevier, vol. 153(C), pages 13-23.
    14. Aida Sa & Patrik Thollander & Enrico Cagno & Majid Rafiee, 2018. "Assessing Swedish Foundries Energy Management Program," Energies, MDPI, vol. 11(10), pages 1-13, October.
    15. Poormoaied, Saeed & Atan, Zümbül, 2020. "A multi-attribute utility theory approach to ordering policy for perishable items," International Journal of Production Economics, Elsevier, vol. 225(C).
    16. Qiu, Ruozhen & Sun, Minghe & Lim, Yun Fong, 2017. "Optimizing (s, S) policies for multi-period inventory models with demand distribution uncertainty: Robust dynamic programing approaches," European Journal of Operational Research, Elsevier, vol. 261(3), pages 880-892.
    17. Gómez-Calvet, Roberto & Conesa, David & Gómez-Calvet, Ana Rosa & Tortosa-Ausina, Emili, 2014. "Energy efficiency in the European Union: What can be learned from the joint application of directional distance functions and slacks-based measures?," Applied Energy, Elsevier, vol. 132(C), pages 137-154.
    18. Matteo Piccioni & Fabrizio Martini & Chiara Martini & Claudia Toro, 2024. "Evaluation of Energy Performance Indicators and Energy Saving Opportunities for the Italian Rubber Manufacturing Industry," Energies, MDPI, vol. 17(7), pages 1-23, March.
    19. Borgonovo, Emanuele & Gatti, Stefano, 2013. "Risk analysis with contractual default. Does covenant breach matter?," European Journal of Operational Research, Elsevier, vol. 230(2), pages 431-443.
    20. Dan A. Iancu & Marek Petrik & Dharmashankar Subramanian, 2015. "Tight Approximations of Dynamic Risk Measures," Mathematics of Operations Research, INFORMS, vol. 40(3), pages 655-682, March.

    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:174:y:2016:i:c:p:88-100. 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.