IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i13p7354-d586094.html
   My bibliography  Save this article

A Profit Distribution Model of Reverse Logistics Based on Fuzzy DEA Efficiency—Modified Shapley Value

Author

Listed:
  • Jiekun Song

    (School of Economics and Management, China University of Petroleum, Qingdao 266580, China)

  • Xiaoping Ma

    (School of Economics and Management, China University of Petroleum, Qingdao 266580, China)

  • Rui Chen

    (School of Economics and Management, China University of Petroleum, Qingdao 266580, China)

Abstract

Reverse logistics is an important way to realize sustainable production and consumption. With the emergence of professional third-party reverse logistics service providers, the outsourcing model has become the main mode of reverse logistics. Whether the distribution of cooperative profit among multiple participants is fair or not determines the quality of the implementation of the outsourcing mode. The traditional Shapley value model is often used to distribute cooperative profit. Since its distribution basis is the marginal profit contribution of each member enterprise to different alliances, it is necessary to estimate the profit of each alliance. However, it is difficult to ensure the accuracy of this estimation, which makes the distribution lack of objectivity. Once the actual profit share deviates from the expectation of member enterprise, the sustainability of the reverse logistics alliance will be affected. This study considers the marginal efficiency contribution of each member enterprise to the alliance and applies it to replace the marginal profit contribution. As the input and output data of reverse logistics cannot be accurately separated from those of the whole enterprise, they are often uncertain. In this paper, we assume that each member enterprise’s input and output data are fuzzy numbers and construct an efficiency measurement model based on fuzzy DEA. Then, we define the characteristic function of alliance and propose a modified Shapley value model to fairly distribute cooperative profit. Finally, an example comprising of two manufacturing enterprises, one sales enterprise, and one third-party reverse logistics service provider is put forward to verify the model’s feasibility and effectiveness. This paper provides a reference for the profit distribution of the reverse logistics.

Suggested Citation

  • Jiekun Song & Xiaoping Ma & Rui Chen, 2021. "A Profit Distribution Model of Reverse Logistics Based on Fuzzy DEA Efficiency—Modified Shapley Value," Sustainability, MDPI, vol. 13(13), pages 1-20, June.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:13:p:7354-:d:586094
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/13/7354/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/13/7354/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Panda, S. & Modak, N.M. & Basu, M. & Goyal, S.K., 2015. "Channel coordination and profit distribution in a social responsible three-layer supply chain," International Journal of Production Economics, Elsevier, vol. 168(C), pages 224-233.
    2. Atay, Ata & Solymosi, Tamás, 2018. "On bargaining sets of supplier-firm-buyer games," Economics Letters, Elsevier, vol. 167(C), pages 99-103.
    3. An, Qingxian & Wen, Yao & Ding, Tao & Li, Yongli, 2019. "Resource sharing and payoff allocation in a three-stage system: Integrating network DEA with the Shapley value method," Omega, Elsevier, vol. 85(C), pages 16-25.
    4. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    5. Xu, Xiaofeng & Wang, Chenglong & Zhou, Peng, 2021. "GVRP considered oil-gas recovery in refined oil distribution: From an environmental perspective," International Journal of Production Economics, Elsevier, vol. 235(C).
    6. Wakana Kato & Ikuo Arizono & Yasuhiko Takemoto, 2018. "A proposal of bargaining solution for cooperative contract in a supply chain," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 559-567, March.
    7. Yang, Zhihua & Zhang, Qianwei, 2015. "Resource allocation based on DEA and modified Shapley value," Applied Mathematics and Computation, Elsevier, vol. 263(C), pages 280-286.
    8. Mi Gan & Shuai Yang & Dandan Li & Mingfei Wang & Si Chen & Ronghui Xie & Jiyang Liu, 2018. "A Novel Intensive Distribution Logistics Network Design and Profit Allocation Problem considering Sharing Economy," Complexity, Hindawi, vol. 2018, pages 1-15, April.
    9. Antonello Ignazio Croce & Giuseppe Musolino & Corrado Rindone & Antonino Vitetta, 2020. "Route and Path Choices of Freight Vehicles: A Case Study with Floating Car Data," Sustainability, MDPI, vol. 12(20), pages 1-15, October.
    10. Ning Jiang & Linda Zhang & Yugang Yu, 2015. "Optimizing Cooperative Advertizing, Profit Sharing, and Inventory Policies in a VMI Supply Chain: A Nash Bargaining Model and Hybrid Algorithm," Post-Print hal-01563010, HAL.
    11. Sheu, Jiuh-Biing & Gao, Xiao-Qin, 2014. "Alliance or no alliance—Bargaining power in competing reverse supply chains," European Journal of Operational Research, Elsevier, vol. 233(2), pages 313-325.
    12. Xu, Xiaofeng & Wei, Zhifei & Ji, Qiang & Wang, Chenglong & Gao, Guowei, 2019. "Global renewable energy development: Influencing factors, trend predictions and countermeasures," Resources Policy, Elsevier, vol. 63(C), 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. Yanmin Guan & Na Wang, 2023. "Automatic modelling of networked innovation outsourcing-oriented talent competency in the era of artificial intelligence," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 408-414, February.
    2. Ziyu Chen & Jili Kong, 2023. "Research on Shared Logistics Decision Based on Evolutionary Game and Income Distribution," Sustainability, MDPI, vol. 15(11), pages 1-24, May.
    3. Xiaoyan Zhuo & Hongbing Li, 2022. "A Study on Cost Allocation in Renovation of Old Urban Residential Communities," Sustainability, MDPI, vol. 14(11), pages 1-20, June.

    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. Mehdi Soltanifar & Farhad Hosseinzadeh Lotfi & Hamid Sharafi & Sebastián Lozano, 2022. "Resource allocation and target setting: a CSW–DEA based approach," Annals of Operations Research, Springer, vol. 318(1), pages 557-589, November.
    2. Weiming Liu & Yating Qiu & Lijiang Jia & Hang Zhou, 2022. "Carbon Emissions Trading and Green Technology Innovation—A Quasi-natural Experiment Based on a Carbon Trading Market Pilot," IJERPH, MDPI, vol. 19(24), pages 1-13, December.
    3. Bin Zhang & Qingyao Xin & Min Tang & Niu Niu & Heran Du & Xiqiang Chang & Zhaohua Wang, 2022. "Revenue allocation for interfirm collaboration on carbon emission reduction: complete information in a big data context," Annals of Operations Research, Springer, vol. 316(1), pages 93-116, September.
    4. Chu, Junfei & Wu, Jie & Chu, Chengbin & Zhang, Tinglong, 2020. "DEA-based fixed cost allocation in two-stage systems: Leader-follower and satisfaction degree bargaining game approaches," Omega, Elsevier, vol. 94(C).
    5. Yijing Chu & Yingying Wang & Zucheng Zhang & Shengli Dai, 2022. "Decoupling of Economic Growth and Industrial Water Use in Hubei Province: From an Ecological–Economic Interaction Perspective," Sustainability, MDPI, vol. 14(20), pages 1-15, October.
    6. Jiasen Sun & Yelin Fu & Xiang Ji & Ray Y. Zhong, 2017. "Allocation of emission permits using DEA-game-theoretic model," Operational Research, Springer, vol. 17(3), pages 867-884, October.
    7. Xiong, Beibei & Chen, Haoxun & An, Qingxian & Wu, Jie, 2019. "A multi-objective distance friction minimization model for performance assessment through data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 279(1), pages 132-142.
    8. Qingxian An & Ping Wang & Honglin Yang & Zongrun Wang, 2021. "Fixed cost allocation in two-stage system using DEA from a noncooperative view," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 43(4), pages 1077-1102, December.
    9. Li, Yongjun & Wang, Lizheng & Li, Feng, 2021. "A data-driven prediction approach for sports team performance and its application to National Basketball Association," Omega, Elsevier, vol. 98(C).
    10. You He & Jinrui Zhang & Jie Feng & Guoqing Shi, 2022. "Dynamic Relationship between Green Economy and Energy Utilization Level: Evidence from China," Energies, MDPI, vol. 15(16), pages 1-14, August.
    11. Lívia Torres & Francisco S. Ramos, 2024. "Allocating Benefits Due to Shared Resources Using Shapley Value and Nucleolus in Dynamic Network Data Envelopment Analysis," Mathematics, MDPI, vol. 12(5), pages 1-23, February.
    12. Liming Zhang & Lili Xue & Chenyu Cui & Ji Qi & Jijia Sun & Xingyu Zhou & Qinyang Dai & Kai Zhang, 2021. "Monitoring the Geometry Morphology of Complex Hydraulic Fracture Network by Using a Multiobjective Inversion Algorithm Based on Decomposition," Energies, MDPI, vol. 14(16), pages 1-21, August.
    13. Yunlong Wang & Zhiting Liu & Xinru Huang & Haizhou Lv & Yun Wu & Kai Zhou, 2022. "A Method for Grading the Hidden Dangers of Urban Gas Polyethylene Pipelines Based on Improved PLC Methods," Energies, MDPI, vol. 15(16), pages 1-15, August.
    14. An, Qingxian & Wang, Ping & Emrouznejad, Ali & Hu, Junhua, 2020. "Fixed cost allocation based on the principle of efficiency invariance in two-stage systems," European Journal of Operational Research, Elsevier, vol. 283(2), pages 662-675.
    15. Yang, Jiawei & Li, Yuanyu & Fang, Lei, 2023. "Financing capacity planning with environmental considerations: A non-parametric analysis," Omega, Elsevier, vol. 118(C).
    16. Fukuyama, Hirofumi & Tsionas, Mike & Tan, Yong, 2023. "Dynamic network data envelopment analysis with a sequential structure and behavioural-causal analysis: Application to the Chinese banking industry," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1360-1373.
    17. Khezrimotlagh, Dariush & Kaffash, Sepideh & Zhu, Joe, 2022. "U.S. airline mergers’ performance and productivity change," Journal of Air Transport Management, Elsevier, vol. 102(C).
    18. Christian Growitsch & Tooraj Jamasb & Christine Müller & Matthias Wissner, 2016. "Social Cost Efficient Service Quality: Integrating Customer Valuation in Incentive Regulation—Evidence from the Case of Norway," International Series in Operations Research & Management Science, in: Joe Zhu (ed.), Data Envelopment Analysis, chapter 0, pages 71-91, Springer.
    19. Franz R. Hahn, 2007. "Determinants of Bank Efficiency in Europe. Assessing Bank Performance Across Markets," WIFO Studies, WIFO, number 31499, April.
    20. Alperovych, Yan & Hübner, Georges & Lobet, Fabrice, 2015. "How does governmental versus private venture capital backing affect a firm's efficiency? Evidence from Belgium," Journal of Business Venturing, Elsevier, vol. 30(4), pages 508-525.

    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:13:y:2021:i:13:p:7354-:d:586094. 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.