IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v164y2015icp433-444.html
   My bibliography  Save this article

A carbon market sensitive optimization model for integrated forward–reverse logistics

Author

Listed:
  • Choudhary, Alok
  • Sarkar, Sagar
  • Settur, Srikar
  • Tiwari, M.K.

Abstract

Globalized supply chains, volatile energy and material prices, increased carbon regulations and competitive marketing pressure for environmental sustainability are driving supply chain decision makers to reduce carbon emissions. Enterprises face the necessity and the challenge of implementing strategies to reduce their supply chain environmental impact in order to remain competitive. One of the most important strategic issues in this context is the configuration of the logistics network. The decision concerning the design of an optimal network of the supply chain plays a vital role in determining the total carbon footprint across the supply chain and also the total cost. Therefore, the logistics network should be designed in a way that it could reduce both the cost and the carbon footprint across the supply chain. In this context, this research proposes a quantitative optimization model for integrated forward–reverse logistics with carbon-footprint considerations, by integrating the carbon emission into a quantitative operational decision-making model with regard to facility layout decisions. The proposed research incorporates carbon emission parameters with various decision variables and modifies traditional integrated forward/reverse logistics model into decision-making quantitative operational model, minimizing both the total cost and the carbon footprint. The proposed model investigates the extent to which carbon reduction requirements can be addressed under a particular set of parameters such as customer demand, rate of return of products etc., by selecting proper policy as an alternative to the costly investment in carbon-reducing technologies. To solve the quantitative model, this research implements a modified and efficient forest data structure to derive the optimal network configuration, minimizing both the cost and the total carbon footprint of the network. A comparative analysis shows the outperformance of the proposed approach over the conventional Genetic Algorithm (GA) for large problem sizes.

Suggested Citation

  • Choudhary, Alok & Sarkar, Sagar & Settur, Srikar & Tiwari, M.K., 2015. "A carbon market sensitive optimization model for integrated forward–reverse logistics," International Journal of Production Economics, Elsevier, vol. 164(C), pages 433-444.
  • Handle: RePEc:eee:proeco:v:164:y:2015:i:c:p:433-444
    DOI: 10.1016/j.ijpe.2014.08.015
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925527314002734
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ijpe.2014.08.015?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. Listes, O.L. & Dekker, R., 2001. "Stochastic approaches for product recovery network design: a case study," Econometric Institute Research Papers EI 2001-08, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    2. Barros, A. I. & Dekker, R. & Scholten, V., 1998. "A two-level network for recycling sand: A case study," European Journal of Operational Research, Elsevier, vol. 110(2), pages 199-214, October.
    3. Giannikos, Ioannis, 1998. "A multiobjective programming model for locating treatment sites and routing hazardous wastes," European Journal of Operational Research, Elsevier, vol. 104(2), pages 333-342, January.
    4. Hua, Guowei & Cheng, T.C.E. & Wang, Shouyang, 2011. "Managing carbon footprints in inventory management," International Journal of Production Economics, Elsevier, vol. 132(2), pages 178-185, August.
    5. Harris, Irina & Naim, Mohamed & Palmer, Andrew & Potter, Andrew & Mumford, Christine, 2011. "Assessing the impact of cost optimization based on infrastructure modelling on CO2 emissions," International Journal of Production Economics, Elsevier, vol. 131(1), pages 313-321, May.
    6. Sundarakani, Balan & de Souza, Robert & Goh, Mark & Wagner, Stephan M. & Manikandan, Sushmera, 2010. "Modeling carbon footprints across the supply chain," International Journal of Production Economics, Elsevier, vol. 128(1), pages 43-50, November.
    7. Zhang, Bin & Xu, Liang, 2013. "Multi-item production planning with carbon cap and trade mechanism," International Journal of Production Economics, Elsevier, vol. 144(1), pages 118-127.
    8. Collins, Julie, 2007. "Climate Change and Emissions Trading (Power Point)," 2007 Seminar, August 24, 2007, Wellington, New Zealand 97617, New Zealand Agricultural and Resource Economics Society.
    9. V Jayaraman & V D R Guide & R Srivastava, 1999. "A closed-loop logistics model for remanufacturing," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(5), pages 497-508, May.
    10. Penkuhn, T. & Spengler, Th. & Puchert, H. & Rentz, O., 1997. "Environmental integrated production planning for the ammonia synthesis," European Journal of Operational Research, Elsevier, vol. 97(2), pages 327-336, March.
    11. Du, Shaofu & Zhu, Lili & Liang, Liang & Ma, Fang, 2013. "Emission-dependent supply chain and environment-policy-making in the ‘cap-and-trade’ system," Energy Policy, Elsevier, vol. 57(C), pages 61-67.
    12. Sheu, Jiuh-Biing & Chou, Yi-Hwa & Hu, Chun-Chia, 2005. "An integrated logistics operational model for green-supply chain management," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 41(4), pages 287-313, July.
    13. Caruso, C. & Colorni, A. & Paruccini, M., 1993. "The regional urban solid waste management system: A modelling approach," European Journal of Operational Research, Elsevier, vol. 70(1), pages 16-30, October.
    14. Gottinger, Hans W., 1988. "A computational model for solid waste management with application," European Journal of Operational Research, Elsevier, vol. 35(3), pages 350-364, June.
    15. 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.
    16. Hu, Tung-Lai & Sheu, Jiuh-Biing & Huang, Kuan-Hsiung, 2002. "A reverse logistics cost minimization model for the treatment of hazardous wastes," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 38(6), pages 457-473, November.
    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. Xu, Xiaoping & He, Ping & Xu, Hao & Zhang, Quanpeng, 2017. "Supply chain coordination with green technology under cap-and-trade regulation," International Journal of Production Economics, Elsevier, vol. 183(PB), pages 433-442.
    2. Salema, Maria Isabel Gomes & Barbosa-Povoa, Ana Paula & Novais, Augusto Q., 2007. "An optimization model for the design of a capacitated multi-product reverse logistics network with uncertainty," European Journal of Operational Research, Elsevier, vol. 179(3), pages 1063-1077, June.
    3. Longfei He & Chenglin Hu & Daozhi Zhao & Haili Lu & Xiaoxi Fu & Yiyu Li, 2016. "Carbon emission mitigation through regulatory policies and operations adaptation in supply chains: theoretic developments and extensions," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 84(1), pages 179-207, November.
    4. Meng, Xiaoge & Yao, Zhong & Nie, Jiajia & Zhao, Yingxue & Li, Zenglu, 2018. "Low-carbon product selection with carbon tax and competition: Effects of the power structure," International Journal of Production Economics, Elsevier, vol. 200(C), pages 224-230.
    5. Liangjie Xia & Tingting Guo & Juanjuan Qin & Xiaohang Yue & Ning Zhu, 2018. "Carbon emission reduction and pricing policies of a supply chain considering reciprocal preferences in cap-and-trade system," Annals of Operations Research, Springer, vol. 268(1), pages 149-175, September.
    6. Abdelkader Sbihi & Richard Eglese, 2010. "Combinatorial optimization and Green Logistics," Annals of Operations Research, Springer, vol. 175(1), pages 159-175, March.
    7. Dekker, Rommert & Bloemhof, Jacqueline & Mallidis, Ioannis, 2012. "Operations Research for green logistics – An overview of aspects, issues, contributions and challenges," European Journal of Operational Research, Elsevier, vol. 219(3), pages 671-679.
    8. Zhitao Xu & Adel Elomri & Shaligram Pokharel & Fatih Mutlu, 2019. "The Design of Green Supply Chains under Carbon Policies: A Literature Review of Quantitative Models," Sustainability, MDPI, vol. 11(11), pages 1-20, May.
    9. Xu, Xiaoping & Zhang, Wei & He, Ping & Xu, Xiaoyan, 2017. "Production and pricing problems in make-to-order supply chain with cap-and-trade regulation," Omega, Elsevier, vol. 66(PB), pages 248-257.
    10. Qiang Du & Jiajie Zhou, 2022. "Evolution of Low Carbon Supply Chain Research: A Systematic Bibliometric Analysis," IJERPH, MDPI, vol. 19(23), pages 1-20, November.
    11. Shaojian Qu & Guoqing Jiang & Ying Ji & Guangming Zhang & Nabe Mohamed, 2021. "Newsvendor’s optimal decisions under stochastic demand and cap-and-trade regulation," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(12), pages 17764-17787, December.
    12. Xu, Song & Govindan, Kannan & Wang, Wanru & Yang, Wenting, 2024. "Supply chain management under cap-and-trade regulation: A literature review and research opportunities," International Journal of Production Economics, Elsevier, vol. 271(C).
    13. Diabat, Ali & Kannan, Devika & Kaliyan, Mathiyazhagan & Svetinovic, Davor, 2013. "An optimization model for product returns using genetic algorithms and artificial immune system," Resources, Conservation & Recycling, Elsevier, vol. 74(C), pages 156-169.
    14. Palak, Gökçe & Ekşioğlu, Sandra Duni & Geunes, Joseph, 2014. "Analyzing the impacts of carbon regulatory mechanisms on supplier and mode selection decisions: An application to a biofuel supply chain," International Journal of Production Economics, Elsevier, vol. 154(C), pages 198-216.
    15. Mallidis, Ioannis & Vlachos, Dimitrios & Iakovou, Eleftherios & Dekker, Rommert, 2014. "Design and planning for green global supply chains under periodic review replenishment policies," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 72(C), pages 210-235.
    16. Min, Hokey & Ko, Hyun-Jeung, 2008. "The dynamic design of a reverse logistics network from the perspective of third-party logistics service providers," International Journal of Production Economics, Elsevier, vol. 113(1), pages 176-192, May.
    17. Chang, Xiangyun & Xia, Haiyang & Zhu, Huiyun & Fan, Tijun & Zhao, Hongqing, 2015. "Production decisions in a hybrid manufacturing–remanufacturing system with carbon cap and trade mechanism," International Journal of Production Economics, Elsevier, vol. 162(C), pages 160-173.
    18. Ping He & Guowei Dou & Wei Zhang, 2017. "Optimal production planning and cap setting under cap-and-trade regulation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(9), pages 1094-1105, September.
    19. Yi Zheng & Gaoxun Zhang & Weiwei Zhang, 2018. "A Duopoly Manufacturers’ Game Model Considering Green Technology Investment under a Cap-and-Trade System," Sustainability, MDPI, vol. 10(3), pages 1-11, March.
    20. 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.

    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:proeco:v:164:y:2015:i:c:p:433-444. 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/locate/ijpe .

    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.