IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v290y2020i1d10.1007_s10479-017-2696-8.html
   My bibliography  Save this article

Weight reduction technology and supply chain network design under carbon emission restriction

Author

Listed:
  • Shuihua Han

    (Xiamen University)

  • Yue Jiang

    (Fujian University of Technology)

  • Ling Zhao

    (Xiamen University)

  • Stephen C. H. Leung

    (University of Hong Kong)

  • Zongwei Luo

    (Southern University of Science and Technology)

Abstract

As policies and regulations related to environmental protection and resource constraints are becoming increasingly tougher, corporations may face the difficulty of determining the optimal trade-offs between economic performance and environmental concerns when selecting product technology and designing supply chain networks. This paper considers weight reduction technology selection and network design problem in a real-world corporation in China which produces, sells and recycles polyethylene terephthalate (PET) bottles used for soft drinks. The problem is addressed while taking consideration of future regulations of carbon emissions restrictions. First, a deterministic mixed-integer linear programming model is developed to analyze the influence of economic cost and carbon emissions for different selections in terms of the weight of PET bottle, raw material purchasing, vehicle routing, facility location, manufacturing and recycling plans, etc. Then, the robust counterpart of the proposed mixed-integer linear programming model is used to deal with the uncertainty in supply chain network resulting from the weight reduction. Finally, results show that though weight reduction is both cost-effective and environmentally beneficial, the increased cost due to the switching of the filling procedure from hot-filling to aseptic cold-filling and the incumbent uncertainties have impacts on the location of the Pareto frontier. Besides, we observe that the feasible range between economic cost and carbon emission shrinks with weightreduction; and the threshold of restricted volume of carbon emission decreases with the increase of uncertainty in the supply chain network.

Suggested Citation

  • Shuihua Han & Yue Jiang & Ling Zhao & Stephen C. H. Leung & Zongwei Luo, 2020. "Weight reduction technology and supply chain network design under carbon emission restriction," Annals of Operations Research, Springer, vol. 290(1), pages 567-590, July.
  • Handle: RePEc:spr:annopr:v:290:y:2020:i:1:d:10.1007_s10479-017-2696-8
    DOI: 10.1007/s10479-017-2696-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-017-2696-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10479-017-2696-8?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. Frota Neto, J. Quariguasi & Bloemhof-Ruwaard, J.M. & van Nunen, J.A.E.E. & van Heck, E., 2008. "Designing and evaluating sustainable logistics networks," International Journal of Production Economics, Elsevier, vol. 111(2), pages 195-208, February.
    2. John Stranlund, 2007. "The regulatory choice of noncompliance in emissions trading programs," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 38(1), pages 99-117, September.
    3. Nouira, Imen & Hammami, Ramzi & Frein, Yannick & Temponi, Cecilia, 2016. "Design of forward supply chains: Impact of a carbon emissions-sensitive demand," International Journal of Production Economics, Elsevier, vol. 173(C), pages 80-98.
    4. Sabri, Ehap H. & Beamon, Benita M., 2000. "A multi-objective approach to simultaneous strategic and operational planning in supply chain design," Omega, Elsevier, vol. 28(5), pages 581-598, October.
    5. Chialin Chen, 2001. "Design for the Environment: A Quality-Based Model for Green Product Development," Management Science, INFORMS, vol. 47(2), pages 250-263, February.
    6. Shenle Pan & Eric Ballot & Frédéric Fontane, 2013. "The reduction of greenhouse gas emissions from freight transport by pooling supply chains," Post-Print hal-00733678, HAL.
    7. Chun-Hung Chiu & Tsan-Ming Choi, 2016. "Supply chain risk analysis with mean-variance models: a technical review," Annals of Operations Research, Springer, vol. 240(2), pages 489-507, May.
    8. Kannan Govindan & R. Sivakumar, 2016. "Green supplier selection and order allocation in a low-carbon paper industry: integrated multi-criteria heterogeneous decision-making and multi-objective linear programming approaches," Annals of Operations Research, Springer, vol. 238(1), pages 243-276, March.
    9. Aharon Ben-Tal & Boaz Golany & Arkadi Nemirovski & Jean-Philippe Vial, 2005. "Retailer-Supplier Flexible Commitments Contracts: A Robust Optimization Approach," Manufacturing & Service Operations Management, INFORMS, vol. 7(3), pages 248-271, February.
    10. M. Fattahi & M. Mahootchi & S. M. Moattar Husseini, 2016. "Integrated strategic and tactical supply chain planning with price-sensitive demands," Annals of Operations Research, Springer, vol. 242(2), pages 423-456, July.
    11. Yu, Chian-Son & Li, Han-Lin, 2000. "A robust optimization model for stochastic logistic problems," International Journal of Production Economics, Elsevier, vol. 64(1-3), pages 385-397, March.
    12. Garud N. Iyengar, 2005. "Robust Dynamic Programming," Mathematics of Operations Research, INFORMS, vol. 30(2), pages 257-280, May.
    13. Chaabane, A. & Ramudhin, A. & Paquet, M., 2012. "Design of sustainable supply chains under the emission trading scheme," International Journal of Production Economics, Elsevier, vol. 135(1), pages 37-49.
    14. Leung, Stephen C.H. & Tsang, Sally O.S. & Ng, W.L. & Wu, Yue, 2007. "A robust optimization model for multi-site production planning problem in an uncertain environment," European Journal of Operational Research, Elsevier, vol. 181(1), pages 224-238, August.
    15. Kannan Govindan & R. Sivakumar, 2016. "Green supplier selection and order allocation in a low-carbon paper industry: integrated multi-criteria heterogeneous decision-making and multi-objective linear programming approaches," Annals of Operations Research, Springer, vol. 238(1), pages 243-276, March.
    16. Dimitris Bertsimas & Melvyn Sim, 2004. "The Price of Robustness," Operations Research, INFORMS, vol. 52(1), pages 35-53, February.
    17. S C H Leung & K K Lai & W-L Ng & Y Wu, 2007. "A robust optimization model for production planning of perishable products," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(4), pages 413-422, April.
    18. Peyman Taki & Farnaz Barzinpour & Ebrahim Teimoury, 2016. "Risk-pooling strategy, lead time, delivery reliability and inventory control decisions in a stochastic multi-objective supply chain network design," Annals of Operations Research, Springer, vol. 244(2), pages 619-646, September.
    19. Xiaoping Li & Dan Zhu, 2011. "Object technology software selection: a case study," Annals of Operations Research, Springer, vol. 185(1), pages 5-24, May.
    20. Morteza Lalmazloumian & Kuan Yew Wong & Kannan Govindan & Devika Kannan, 2016. "A robust optimization model for agile and build-to-order supply chain planning under uncertainties," Annals of Operations Research, Springer, vol. 240(2), pages 435-470, May.
    21. Konstantinos Petridis, 2015. "Optimal design of multi-echelon supply chain networks under normally distributed demand," Annals of Operations Research, Springer, vol. 227(1), pages 63-91, April.
    22. Ciwei Dong & Bin Shen & Pui-Sze Chow & Liu Yang & Chi To Ng, 2016. "Sustainability investment under cap-and-trade regulation," Annals of Operations Research, Springer, vol. 240(2), pages 509-531, May.
    23. Pan, Shenle & Ballot, Eric & Fontane, Frédéric, 2013. "The reduction of greenhouse gas emissions from freight transport by pooling supply chains," International Journal of Production Economics, Elsevier, vol. 143(1), pages 86-94.
    24. John M. Mulvey & Robert J. Vanderbei & Stavros A. Zenios, 1995. "Robust Optimization of Large-Scale Systems," Operations Research, INFORMS, vol. 43(2), pages 264-281, 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. Yanfen Mu & Feng Niu, 2022. "To Be or Not to Be? Strategic Analysis of Carbon Tax Guiding Manufacturers to Choose Low-Carbon Technology," Sustainability, MDPI, vol. 14(22), pages 1-16, November.
    2. Jahani, Hamed & Abbasi, Babak & Sheu, Jiuh-Biing & Klibi, Walid, 2024. "Supply chain network design with financial considerations: A comprehensive review," European Journal of Operational Research, Elsevier, vol. 312(3), pages 799-839.

    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. Jabbarzadeh, Armin & Haughton, Michael & Pourmehdi, Fahime, 2019. "A robust optimization model for efficient and green supply chain planning with postponement strategy," International Journal of Production Economics, Elsevier, vol. 214(C), pages 266-283.
    2. Shuihua Han & Weina Ma & Ling Zhao & Xuelian Zhang & Ming K. Lim & Shuangyuan Yang & Stephen Leung, 2016. "A robust optimisation model for hybrid remanufacturing and manufacturing systems under uncertain return quality and market demand," International Journal of Production Research, Taylor & Francis Journals, vol. 54(17), pages 5056-5072, September.
    3. Behzadi, Golnar & O’Sullivan, Michael Justin & Olsen, Tava Lennon & Zhang, Abraham, 2018. "Agribusiness supply chain risk management: A review of quantitative decision models," Omega, Elsevier, vol. 79(C), pages 21-42.
    4. Pereira, Daniel Filipe & Oliveira, José Fernando & Carravilla, Maria Antónia, 2023. "Design of a sales plan in a hybrid contractual and non-contractual context in a setting of limited capacity: A robust approach," International Journal of Production Economics, Elsevier, vol. 260(C).
    5. Saberi, Sara, 2018. "Sustainable, multiperiod supply chain network model with freight carrier through reduction in pollution stock," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 118(C), pages 421-444.
    6. Morteza Lalmazloumian & M. Fazle Baki & Majid Ahmadi, 2023. "A two-stage stochastic optimization framework to allocate operating room capacity in publicly-funded hospitals under uncertainty," Health Care Management Science, Springer, vol. 26(2), pages 238-260, June.
    7. Ratanakuakangwan, Sudlop & Morita, Hiroshi, 2021. "Hybrid stochastic robust optimization and robust optimization for energy planning – A social impact-constrained case study," Applied Energy, Elsevier, vol. 298(C).
    8. Jabbarzadeh, Armin & Fahimnia, Behnam & Seuring, Stefan, 2014. "Dynamic supply chain network design for the supply of blood in disasters: A robust model with real world application," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 70(C), pages 225-244.
    9. Aleksander Banasik & Jacqueline M. Bloemhof-Ruwaard & Argyris Kanellopoulos & G. D. H. Claassen & Jack G. A. J. Vorst, 2018. "Multi-criteria decision making approaches for green supply chains: a review," Flexible Services and Manufacturing Journal, Springer, vol. 30(3), pages 366-396, September.
    10. Mohammaddust, Faeghe & Rezapour, Shabnam & Farahani, Reza Zanjirani & Mofidfar, Mohammad & Hill, Alex, 2017. "Developing lean and responsive supply chains: A robust model for alternative risk mitigation strategies in supply chain designs," International Journal of Production Economics, Elsevier, vol. 183(PC), pages 632-653.
    11. Wei, Cansheng & Li, Yongjian & Cai, Xiaoqiang, 2011. "Robust optimal policies of production and inventory with uncertain returns and demand," International Journal of Production Economics, Elsevier, vol. 134(2), pages 357-367, December.
    12. Baghalian, Atefeh & Rezapour, Shabnam & Farahani, Reza Zanjirani, 2013. "Robust supply chain network design with service level against disruptions and demand uncertainties: A real-life case," European Journal of Operational Research, Elsevier, vol. 227(1), pages 199-215.
    13. Hashem Omrani & Farzane Adabi & Narges Adabi, 2017. "Designing an efficient supply chain network with uncertain data: a robust optimization—data envelopment analysis approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(7), pages 816-828, July.
    14. Donya Rahmani & Arash Zandi & Sara Behdad & Arezou Entezaminia, 2021. "A light robust model for aggregate production planning with consideration of environmental impacts of machines," Operational Research, Springer, vol. 21(1), pages 273-297, March.
    15. Ozden Tozanli & Gazi Murat Duman & Elif Kongar & Surendra M. Gupta, 2017. "Environmentally Concerned Logistics Operations in Fuzzy Environment: A Literature Survey," Logistics, MDPI, vol. 1(1), pages 1-42, June.
    16. Sun, Huali & Li, Jiamei & Wang, Tingsong & Xue, Yaofeng, 2022. "A novel scenario-based robust bi-objective optimization model for humanitarian logistics network under risk of disruptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    17. Surya Prakash & Sameer Kumar & Gunjan Soni & Vipul Jain & Ajay Pal Singh Rathore, 2020. "Closed-loop supply chain network design and modelling under risks and demand uncertainty: an integrated robust optimization approach," Annals of Operations Research, Springer, vol. 290(1), pages 837-864, July.
    18. Roya Soltani & Seyed J Sadjadi, 2014. "Reliability optimization through robust redundancy allocation models with choice of component type under fuzziness," Journal of Risk and Reliability, , vol. 228(5), pages 449-459, October.
    19. Mirzapour Al-e-hashem, S.M.J. & Malekly, H. & Aryanezhad, M.B., 2011. "A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty," International Journal of Production Economics, Elsevier, vol. 134(1), pages 28-42, November.
    20. Shishebori, Davood & Yousefi Babadi, Abolghasem, 2015. "Robust and reliable medical services network design under uncertain environment and system disruptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 77(C), pages 268-288.

    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:spr:annopr:v:290:y:2020:i:1:d:10.1007_s10479-017-2696-8. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.