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An improved co-evolutionary algorithm for green manufacturing by integration of recovery option selection and disassembly planning for end-of-life products

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  • Kai Meng
  • Peihuang Lou
  • Xianghui Peng
  • Victor Prybutok

Abstract

There is a strong need for recovery decision-making for end-of-life (EOL) products to satisfy sustainable manufacturing requirements. This paper develops and tests a profit maximisation model by simultaneously integrating recovery option selection and disassembly planning. The proposed model considers the quality of EOL components. This paper utilises an integrated method of multi-target reverse recursion and partial topological sorting to generate a feasible EOL solution that also reduces the complexity of genetic constraints handling. In order to determine recovery options, disassembly level and disassembly sequence simultaneously, this paper develops an improved co-evolutionary algorithm (ICA) to search for an optimal EOL solution. The proposed algorithm adopts the evolutionary mechanism of localised interaction and endosymbiotic competition. Further, an advanced local search operator is introduced to improve convergence performance, and a global disturbance strategy is also suggested to prevent premature convergence. Finally, this paper conducts a series of computational experiments under various scenarios to validate the meta-heuristic integrated decision-making model proposed and the superiority of the developed ICA. The results show that the proposed approach offers a strong and flexible decision support tool for intelligent recovery management in a ubiquitous information environment. We discuss the theoretical and practical contributions of this paper and implications for future research.

Suggested Citation

  • Kai Meng & Peihuang Lou & Xianghui Peng & Victor Prybutok, 2016. "An improved co-evolutionary algorithm for green manufacturing by integration of recovery option selection and disassembly planning for end-of-life products," International Journal of Production Research, Taylor & Francis Journals, vol. 54(18), pages 5567-5593, September.
  • Handle: RePEc:taf:tprsxx:v:54:y:2016:i:18:p:5567-5593
    DOI: 10.1080/00207543.2016.1176263
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    Citations

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    Cited by:

    1. Choudhary, Divya & Qaiser, Fahham Hasan & Choudhary, Alok & Fernandes, Kiran, 2022. "A model for managing returns in a circular economy context: A case study from the Indian electronics industry," International Journal of Production Economics, Elsevier, vol. 249(C).
    2. Qian-wang Deng & Hao-lan Liao & Bo-wen Xu & Xia-hui Liu, 2017. "The Resource Benefits Evaluation Model on Remanufacturing Processes of End-of-Life Construction Machinery under the Uncertainty in Recycling Price," Sustainability, MDPI, vol. 9(2), pages 1-21, February.
    3. Gunasekara, Lahiru & Robb, David J. & Zhang, Abraham, 2023. "Used product acquisition, sorting and disposition for circular supply chains: Literature review and research directions," International Journal of Production Economics, Elsevier, vol. 260(C).
    4. Meng, Kai & Cao, Ying & Peng, Xianghui & Prybutok, Victor & Gupta, Varun, 2020. "Demand-dependent recovery decision-making of a batch of products for sustainability," International Journal of Production Economics, Elsevier, vol. 224(C).
    5. Mehmet Ali Ilgin & Hakan Akçay & Ceyhun Araz, 2017. "Disassembly line balancing using linear physical programming," International Journal of Production Research, Taylor & Francis Journals, vol. 55(20), pages 6108-6119, October.
    6. Meng, Kai & Lou, Peihuang & Peng, Xianghui & Prybutok, Victor, 2017. "Multi-objective optimization decision-making of quality dependent product recovery for sustainability," International Journal of Production Economics, Elsevier, vol. 188(C), pages 72-85.
    7. Mojtaba M. Shourkaei & Kelsey M. Taylor & Bruno Dyck, 2024. "Examining sustainable supply chain management via a social‐symbolic work lens: Lessons from Patagonia," Business Strategy and the Environment, Wiley Blackwell, vol. 33(2), pages 1477-1496, February.

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