IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i16p12583-d1220502.html
   My bibliography  Save this article

An Integrated Optimization Model of Green Supply Chain Network Design with Inventory Management

Author

Listed:
  • Lingyun Zhou

    (Transportation & Economics Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China)

  • Dezhi Zhang

    (School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China)

  • Shuangyan Li

    (College of Logistics and Transportation, Central South University of Forestry and Technology, Changsha 410004, China)

  • Xiangyu Luo

    (School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China)

Abstract

Supply chain network design and inventory management are both significant for improving the core competitiveness of enterprises. This study investigates the joint optimization problem of facility locations and inventory for assembly manufacturing enterprises’ multi-echelon supply chain networks, considering the locations of facilities, the selection of suppliers, transport mode choices, and inventory decisions simultaneously. A corresponding integrated optimization model is proposed, which aims to minimize the total cost, consisting of the fixed open cost of facilities, the inventory cost of the open plants and distribution centers, and the transportation cost of vehicles in the entire supply chain network as well as the cost of CO 2 emissions. Based on the characteristics of the proposed optimization model, a hybrid genetic algorithm embedded with a local search is developed to solve the proposed model. Numerical examples and a case study are provided to illustrate the effectiveness of the proposed model and the corresponding algorithm. The findings show that the model is reasonable and applicable, and hybrid genetic algorithm (HGA) is more efficient than the standard genetic algorithm (SGA). In addition, plants’ maximum lead-time has a significant impact on the total cost of the supply chain.

Suggested Citation

  • Lingyun Zhou & Dezhi Zhang & Shuangyan Li & Xiangyu Luo, 2023. "An Integrated Optimization Model of Green Supply Chain Network Design with Inventory Management," Sustainability, MDPI, vol. 15(16), pages 1-24, August.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:16:p:12583-:d:1220502
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/16/12583/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/16/12583/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bhatnagar, Rohit & Lin, Bing, 2019. "The joint transshipment and production control policies for multi-location production/inventory systems," European Journal of Operational Research, Elsevier, vol. 275(3), pages 957-970.
    2. Vidal, Carlos J. & Goetschalckx, Marc, 1997. "Strategic production-distribution models: A critical review with emphasis on global supply chain models," European Journal of Operational Research, Elsevier, vol. 98(1), pages 1-18, April.
    3. Jung, Seung Hwan & Feng, Tianjun, 2020. "Government subsidies for green technology development under uncertainty," European Journal of Operational Research, Elsevier, vol. 286(2), pages 726-739.
    4. Waltho, Cynthia & Elhedhli, Samir & Gzara, Fatma, 2019. "Green supply chain network design: A review focused on policy adoption and emission quantification," International Journal of Production Economics, Elsevier, vol. 208(C), pages 305-318.
    5. Wang, Changbo & Zhang, Lixiao & Chang, Yuan & Pang, Mingyue, 2015. "Biomass direct-fired power generation system in China: An integrated energy, GHG emissions, and economic evaluation for Salix," Energy Policy, Elsevier, vol. 84(C), pages 155-165.
    6. 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.
    7. Guodong Yu & Fei Li & Yu Yang, 2017. "Robust supply chain networks design and ambiguous risk preferences," International Journal of Production Research, Taylor & Francis Journals, vol. 55(4), pages 1168-1182, February.
    8. Bian, Junsong & Zhang, Guoqing & Zhou, Guanghui, 2020. "Manufacturer vs. Consumer Subsidy with Green Technology Investment and Environmental Concern," European Journal of Operational Research, Elsevier, vol. 287(3), pages 832-843.
    9. Zhou, Xiaoyang & Wei, Xiaoya & Lin, Jun & Tian, Xin & Lev, Benjamin & Wang, Shouyang, 2021. "Supply chain management under carbon taxes: A review and bibliometric analysis," Omega, Elsevier, vol. 98(C).
    10. Tsao, Yu-Chung & Mangotra, Divya & Lu, Jye-Chyi & Dong, Ming, 2012. "A continuous approximation approach for the integrated facility-inventory allocation problem," European Journal of Operational Research, Elsevier, vol. 222(2), pages 216-228.
    11. Leyla Ozsen & Collette R. Coullard & Mark S. Daskin, 2008. "Capacitated warehouse location model with risk pooling," Naval Research Logistics (NRL), John Wiley & Sons, vol. 55(4), pages 295-312, June.
    12. Dezhi Zhang & Shuxin Yang & Shuangyan Li & Jiajun Fan & Bin Ji, 2020. "Integrated Optimization of the Location–Inventory Problem of Maintenance Component Distribution for High-Speed Railway Operations," Sustainability, MDPI, vol. 12(13), pages 1-25, July.
    13. Miranda, Pablo A. & Garrido, Rodrigo A., 2009. "Inventory service-level optimization within distribution network design problem," International Journal of Production Economics, Elsevier, vol. 122(1), pages 276-285, November.
    14. Miranda, Pablo A. & Garrido, Rodrigo A., 2004. "Incorporating inventory control decisions into a strategic distribution network design model with stochastic demand," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 40(3), pages 183-207, May.
    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. Shuangyan Li & Xialian Li & Dezhi Zhang & Lingyun Zhou, 2017. "Joint Optimization of Distribution Network Design and Two-Echelon Inventory Control with Stochastic Demand and CO2 Emission Tax Charges," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-22, January.
    2. Escalona, P. & Marianov, V. & Ordóñez, F. & Stegmaier, R., 2018. "On the effect of inventory policies on distribution network design with several demand classes," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 111(C), pages 229-240.
    3. Darmawan, Agus & Wong, Hartanto & Thorstenson, Anders, 2021. "Supply chain network design with coordinated inventory control," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    4. Aaron Guerrero Campanur & Elias Olivares-Benitez & Pablo A. Miranda & Rodolfo Eleazar Perez-Loaiza & Jose Humberto Ablanedo-Rosas, 2018. "Design of a Logistics Nonlinear System for a Complex, Multiechelon, Supply Chain Network with Uncertain Demands," Complexity, Hindawi, vol. 2018, pages 1-16, November.
    5. Jinxuan Song & Xu Yan, 2023. "Impact of Government Subsidies, Competition, and Blockchain on Green Supply Chain Decisions," Sustainability, MDPI, vol. 15(4), pages 1-27, February.
    6. Juan Zhang & Ziyue Wang & Huiju Zhao, 2020. "The Impact of Consumer Subsidy on Green Technology Innovations for Vehicles and Environmental Impact," IJERPH, MDPI, vol. 17(20), pages 1-24, October.
    7. Tsao, Yu-Chung & Vu, Thuy-Linh, 2019. "Power supply chain network design problem for smart grid considering differential pricing and buy-back policies," Energy Economics, Elsevier, vol. 81(C), pages 493-502.
    8. Bouchery, Yann & Woxenius, Johan & Fransoo, Jan C., 2020. "Identifying the market areas of port-centric logistics and hinterland intermodal transportation," European Journal of Operational Research, Elsevier, vol. 285(2), pages 599-611.
    9. Hsu, Chaug-Ing & Li, Hui-Chieh, 2009. "An integrated plant capacity and production planning model for high-tech manufacturing firms with economies of scale," International Journal of Production Economics, Elsevier, vol. 118(2), pages 486-500, April.
    10. Nathalia Wolf & Pablo Escalona & Mónica López-Campos & Alejandro Angulo & Jorge Weston, 2023. "On Carbon Tax Effectiveness in Inducing a Clean Technology Transition: An Evaluation Based on Optimal Strategic Capacity Planning," Sustainability, MDPI, vol. 15(15), pages 1-23, July.
    11. Jenn-Rong Lin & Linda Nozick & Mark Turnquist, 2006. "Strategic design of distribution systems with economies of scale in transportation," Annals of Operations Research, Springer, vol. 144(1), pages 161-180, April.
    12. Gaigné, C. & Hovelaque, V. & Mechouar, Y., 2020. "Carbon tax and sustainable facility location: The role of production technology," International Journal of Production Economics, Elsevier, vol. 224(C).
    13. Zheng, Shiyuan & Jiang, Changmin & Fu, Xiaowen & Ge, Ying-En & Shu, Jia, 2022. "Subsidies for green technology adoption under uncertain demand and incomplete information," Omega, Elsevier, vol. 112(C).
    14. 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.
    15. Chowdhury, Sudipta & Emelogu, Adindu & Marufuzzaman, Mohammad & Nurre, Sarah G. & Bian, Linkan, 2017. "Drones for disaster response and relief operations: A continuous approximation model," International Journal of Production Economics, Elsevier, vol. 188(C), pages 167-184.
    16. Liu, Samuel Shuai & Hua, Guowei & Ma, Benedict Jun & Cheng, T.C.E., 2023. "Competition between green and non-green products in the blockchain era," International Journal of Production Economics, Elsevier, vol. 264(C).
    17. Hsu, Chaug-Ing & Li, Hui-Chieh, 2011. "Reliability evaluation and adjustment of supply chain network design with demand fluctuations," International Journal of Production Economics, Elsevier, vol. 132(1), pages 131-145, July.
    18. Ke Li & Gengxin Dai & Yanfei Xia & Zongyu Mu & Guitao Zhang & Yangyan Shi, 2022. "Green Technology Investment with Data-Driven Marketing and Government Subsidy in a Platform Supply Chain," Sustainability, MDPI, vol. 14(7), pages 1-22, March.
    19. Ma, Shigui & He, Yong & Gu, Ran & Li, Shanshan, 2021. "Sustainable supply chain management considering technology investments and government intervention," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    20. Ran Feng & Xiaoe Qu, 2023. "Innovation-Driven Industrial Agglomeration Impact on Green Economic Growth in the Yellow River Basin: An Empirical Analysis," Sustainability, MDPI, vol. 15(17), pages 1-24, September.

    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:jsusta:v:15:y:2023:i:16:p:12583-:d:1220502. 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.