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Evolutionary game analysis of cooperation between natural resource- and energy-intensive companies in reverse logistics operations

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  • Gu, Wei
  • Wei, Lirong
  • Zhang, Wenqing
  • Yan, Xiangbin

Abstract

Reverse logistics (RL) plays a significant role in circular economic development, especially in natural resource- and energy-intensive industries (NREI) as a long-term strategy to make a breakthrough in their development. In this study, we construct an evolutionary game theoretical model to investigate the cooperation between NREI companies that adopt one of two reverse logistics operating strategies: self-operation or joint venture. We derive the evolutionary stable strategy of the game through a replicator dynamic system. Simulation experiments are also conducted to validate the theoretical results, to show the evolution, and to analyze the sensitivities to parameters, based on the data of the steel industry that we collected from China. Among many other outcomes, our results show that self-operation is more likely to succeed in operating the current reverse logistics system. The methodologies and results are useful in supporting NREI's reverse logistics decisions. The practical applications of the study can be extended to other NREI sectors when the way of RL implementation is to be decided.

Suggested Citation

  • Gu, Wei & Wei, Lirong & Zhang, Wenqing & Yan, Xiangbin, 2019. "Evolutionary game analysis of cooperation between natural resource- and energy-intensive companies in reverse logistics operations," International Journal of Production Economics, Elsevier, vol. 218(C), pages 159-169.
    • Handle: RePEc:eee:proeco:v:218:y:2019:i:c:p:159-169
    DOI: 10.1016/j.ijpe.2019.05.001
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    Cited by:

    1. Sudusinghe, Jayani Ishara & Seuring, Stefan, 2022. "Supply chain collaboration and sustainability performance in circular economy: A systematic literature review," International Journal of Production Economics, Elsevier, vol. 245(C).
    2. Cricelli, Livio & Greco, Marco & Grimaldi, Michele, 2021. "An investigation on the effect of inter-organizational collaboration on reverse logistics," International Journal of Production Economics, Elsevier, vol. 240(C).
    3. Xin Yao & Yuanyuan Cheng & Li Zhou & Malin Song, 2022. "Green efficiency performance analysis of the logistics industry in China: based on a kind of machine learning methods," Annals of Operations Research, Springer, vol. 308(1), pages 727-752, January.
    4. Lili Ding & Zhimeng Guo & Yuemei Xue, 2023. "Dump or recycle? Consumer's environmental awareness and express package disposal based on an evolutionary game model," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(7), pages 6963-6986, July.
    5. Gu, Wei & Yu, Xiaoru & Zhang, Shichen & Yan, Xiangbin & Wang, Chen, 2023. "To outsource or not: Bike-share rebalancing strategies under the service quality deviation of a third party," European Journal of Operational Research, Elsevier, vol. 310(2), pages 847-859.
    6. Yumei Luo & Yuke Zhang & Lei Yang, 2022. "How to Promote Logistics Enterprises to Participate in Reverse Emergency Logistics: A Tripartite Evolutionary Game Analysis," Sustainability, MDPI, vol. 14(19), pages 1-20, September.
    7. Gu, Wei & Li, Meng & Wang, Chen & Shang, Jennifer & Wei, Lirong, 2021. "Strategic sourcing selection for bike-sharing rebalancing: An evolutionary game approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 156(C).
    8. Stekelorum, Rebecca & Laguir, Issam & Gupta, Shivam & Kumar, Sameer, 2021. "Green supply chain management practices and third-party logistics providers’ performances: A fuzzy-set approach," International Journal of Production Economics, Elsevier, vol. 235(C).

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