IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v318y2024i1p253-268.html
   My bibliography  Save this article

A data-driven distributionally robust optimization approach for the core acquisition problem

Author

Listed:
  • Yang, Cheng-Hu
  • Su, Xiao-Li
  • Ma, Xin
  • Talluri, Srinivas

Abstract

Reusing electric vehicles (EV) batteries that reach the end of their useful first life is an environmental and cost-competitive option; however, the process of recycling EV batteries is not yet mature. Due to complex electrochemical reactions and physical conditions, the quality of used EV batteries (cores) is highly uncertain. The remanufacturer needs to make the acquisition decision under quality distributional ambiguity. Perfect quality distribution of cores cannot be known to the remanufacturer in practice. We develop distributionally robust optimization models based on phi-divergence measures and the imprecise Dirichlet model (DRO-IDM) to derive robust decisions. First, we find that the bounds of quality probability intervals are identified solely based on the collected data by introducing the imprecise Dirichlet model. The derived finite-sample boundary can reduce the scope of the uncertainty set and avoid the no-direction search issue. Second, our models can hedge against distributional uncertainty, reduce the probability of a robust solution that deviates from the optimal solution, and correct bias in decision making. Third, we extend the DRO-IDM to develop data-driven models, that can reassess the value of multisource quality information to improve the estimation accuracy of core quality and maximize the remanufacturer’s profit. Our study provides new insights for remanufacturers: the new remanufacturing process proposed in our work can assist remanufacturers in utilizing the values of cores without disassembly; the information-aware algorithm can offer the remanufacturing sector a valuable tool for efficiently filtering out invalid information in optimizing acquisition decisions; this capability empowers decision-makers to leverage multiple sources of information and expedite the process of digital transformation in remanufacturing; our approach can also provide a manner of integrating information fusion and distribution learning into remanufacturing.

Suggested Citation

  • Yang, Cheng-Hu & Su, Xiao-Li & Ma, Xin & Talluri, Srinivas, 2024. "A data-driven distributionally robust optimization approach for the core acquisition problem," European Journal of Operational Research, Elsevier, vol. 318(1), pages 253-268.
    • Handle: RePEc:eee:ejores:v:318:y:2024:i:1:p:253-268
    DOI: 10.1016/j.ejor.2024.05.007
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221724003448
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2024.05.007?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. Li, Shuangqi & He, Hongwen & Li, Jianwei, 2019. "Big data driven lithium-ion battery modeling method based on SDAE-ELM algorithm and data pre-processing technology," Applied Energy, Elsevier, vol. 242(C), pages 1259-1273.
    2. Atalay Atasu & Charles J. Corbett & Ximin (Natalie) Huang & L. Beril Toktay, 2020. "Sustainable Operations Management Through the Perspective of Manufacturing & Service Operations Management," Manufacturing & Service Operations Management, INFORMS, vol. 22(1), pages 146-157, January.
    3. Xi Chen & David Simchi-Levi & Yining Wang, 2022. "Privacy-Preserving Dynamic Personalized Pricing with Demand Learning," Management Science, INFORMS, vol. 68(7), pages 4878-4898, July.
    4. Fuqiang Zhang & Renyu Zhang, 2018. "Trade-in Remanufacturing, Customer Purchasing Behavior, and Government Policy," Manufacturing & Service Operations Management, INFORMS, vol. 20(4), pages 601-616, October.
    5. Karthik Natarajan & Melvyn Sim & Joline Uichanco, 2018. "Asymmetry and Ambiguity in Newsvendor Models," Management Science, INFORMS, vol. 64(7), pages 3146-3167, July.
    6. Cheng-Hu Yang & Xin Ma & Srinivas Talluri, 2019. "Optimal acquisition decision in a remanufacturing system with partial random yield information," International Journal of Production Research, Taylor & Francis Journals, vol. 57(6), pages 1624-1644, March.
    7. Gendao Li & Baofeng Sun, 2014. "Optimal Dynamic Pricing For Used Products In Remanufacturing Over An Infinite Horizon," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 31(03), pages 1-21.
    8. James D. Abbey & H. Neil Geismar & Gilvan C. Souza, 2019. "Improving Remanufacturing Core Recovery and Profitability Through Seeding," Production and Operations Management, Production and Operations Management Society, vol. 28(3), pages 610-627, March.
    9. Zhu, Mengping & Liu, Zhixue & Li, Jianbin & Zhu, Stuart X., 2020. "Electric vehicle battery capacity allocation and recycling with downstream competition," European Journal of Operational Research, Elsevier, vol. 283(1), pages 365-379.
    10. Aharon Ben-Tal & Dick den Hertog & Anja De Waegenaere & Bertrand Melenberg & Gijs Rennen, 2013. "Robust Solutions of Optimization Problems Affected by Uncertain Probabilities," Management Science, INFORMS, vol. 59(2), pages 341-357, April.
    11. Akshay Mutha & Saurabh Bansal & V. Daniel R. Guide, 2016. "Managing Demand Uncertainty through Core Acquisition in Remanufacturing," Production and Operations Management, Production and Operations Management Society, vol. 25(8), pages 1449-1464, August.
    12. Rahimian, Hamed & Bayraksan, Güzin & Homem-de-Mello, Tito, 2019. "Controlling risk and demand ambiguity in newsvendor models," European Journal of Operational Research, Elsevier, vol. 279(3), pages 854-868.
    13. Liu, Chang-Yi & Wang, Hui & Tang, Juan & Chang, Ching-Ter & Liu, Zhi, 2021. "Optimal recovery model in a used batteries closed-loop supply chain considering uncertain residual capacity," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 156(C).
    14. Gong, Hailei & Zhang, Zhi-Hai, 2022. "Benders decomposition for the distributionally robust optimization of pricing and reverse logistics network design in remanufacturing systems," European Journal of Operational Research, Elsevier, vol. 297(2), pages 496-510.
    15. Wolfram Wiesemann & Daniel Kuhn & Melvyn Sim, 2014. "Distributionally Robust Convex Optimization," Operations Research, INFORMS, vol. 62(6), pages 1358-1376, December.
    16. Srinivas Bollapragada & Thomas E. Morton, 1999. "Myopic Heuristics for the Random Yield Problem," Operations Research, INFORMS, vol. 47(5), pages 713-722, October.
    17. Xiong, Yu & Li, Gendao & Zhou, Yu & Fernandes, Kiran & Harrison, Richard & Xiong, Zhongkai, 2014. "Dynamic pricing models for used products in remanufacturing with lost-sales and uncertain quality," International Journal of Production Economics, Elsevier, vol. 147(PC), pages 678-688.
    18. De Giovanni, Pietro, 2018. "A joint maximization incentive in closed-loop supply chains with competing retailers: The case of spent-battery recycling," European Journal of Operational Research, Elsevier, vol. 268(1), pages 128-147.
    19. V. Daniel R. Guide, Jr. & Ruud H. Teunter & Luk N. Van Wassenhove, 2003. "Matching Demand and Supply to Maximize Profits from Remanufacturing," Manufacturing & Service Operations Management, INFORMS, vol. 5(4), pages 303-316, October.
    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. Kim, Yun Geon & Chung, Byung Do, 2024. "Data-driven Wasserstein distributionally robust dual-sourcing inventory model under uncertain demand," Omega, Elsevier, vol. 127(C).
    2. Liu, Baolong & Papier, Felix, 2022. "Remanufacturing of multi-component systems with product substitution," European Journal of Operational Research, Elsevier, vol. 301(3), pages 896-911.
    3. Guevara, Esnil & Babonneau, Fréderic & Homem-de-Mello, Tito & Moret, Stefano, 2020. "A machine learning and distributionally robust optimization framework for strategic energy planning under uncertainty," Applied Energy, Elsevier, vol. 271(C).
    4. Shanshan Wang & Erick Delage, 2024. "A Column Generation Scheme for Distributionally Robust Multi-Item Newsvendor Problems," INFORMS Journal on Computing, INFORMS, vol. 36(3), pages 849-867, May.
    5. Aditya Vedantam & Ananth Iyer, 2021. "Revenue‐Sharing Contracts Under Quality Uncertainty in Remanufacturing," Production and Operations Management, Production and Operations Management Society, vol. 30(7), pages 2008-2026, July.
    6. Qiu, Ruozhen & Sun, Yue & Sun, Minghe, 2022. "A robust optimization approach for multi-product inventory management in a dual-channel warehouse under demand uncertainties," Omega, Elsevier, vol. 109(C).
    7. Zhi Chen & Weijun Xie, 2021. "Regret in the Newsvendor Model with Demand and Yield Randomness," Production and Operations Management, Production and Operations Management Society, vol. 30(11), pages 4176-4197, November.
    8. Liu, Wenjie & Liu, Wei & Shen, Ningning & Xu, Zhitao & Xie, Naiming & Chen, Jian & Zhou, Huiyu, 2022. "Pricing and collection decisions of a closed-loop supply chain with fuzzy demand," International Journal of Production Economics, Elsevier, vol. 245(C).
    9. Patricia van Loon & Luk N. Van Wassenhove & Ales Mihelic, 2022. "Designing a circular business strategy: 7 years of evolution at a large washing machine manufacturer," Business Strategy and the Environment, Wiley Blackwell, vol. 31(3), pages 1030-1041, March.
    10. Zhang, Abraham & Wang, Jason X. & Farooque, Muhammad & Wang, Yulan & Choi, Tsan-Ming, 2021. "Multi-dimensional circular supply chain management: A comparative review of the state-of-the-art practices and research," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 155(C).
    11. Shunichi Ohmori, 2021. "A Predictive Prescription Using Minimum Volume k -Nearest Neighbor Enclosing Ellipsoid and Robust Optimization," Mathematics, MDPI, vol. 9(2), pages 1-16, January.
    12. L. Jeff Hong & Zhiyuan Huang & Henry Lam, 2021. "Learning-Based Robust Optimization: Procedures and Statistical Guarantees," Management Science, INFORMS, vol. 67(6), pages 3447-3467, June.
    13. Das, Debabrata & Dutta, Pankaj, 2022. "Product return management through promotional offers: The role of consumers’ loss aversion," International Journal of Production Economics, Elsevier, vol. 251(C).
    14. Ma, Deqing & Hu, Jinsong, 2022. "The optimal combination between blockchain and sales format in an internet platform-based closed-loop supply chain," International Journal of Production Economics, Elsevier, vol. 254(C).
    15. Antonio J. Conejo & Nicholas G. Hall & Daniel Zhuoyu Long & Runhao Zhang, 2021. "Robust Capacity Planning for Project Management," INFORMS Journal on Computing, INFORMS, vol. 33(4), pages 1533-1550, October.
    16. Hong Sun & Yan Li, 2023. "Optimal Acquisition and Production Policies for Remanufacturing with Quality Grading," Mathematics, MDPI, vol. 11(7), pages 1-21, March.
    17. Ran Ji & Miguel A. Lejeune, 2021. "Data-Driven Optimization of Reward-Risk Ratio Measures," INFORMS Journal on Computing, INFORMS, vol. 33(3), pages 1120-1137, July.
    18. Chen, Jiumei & Zhang, Wen & Gong, Bengang & Zhang, Xiaoqi & Li, Hongping, 2022. "Optimal policy for the recycling of electric vehicle retired power batteries," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    19. Wang, Fan & Zhang, Chao & Zhang, Hui & Xu, Liang, 2021. "Short-term physician rescheduling model with feature-driven demand for mental disorders outpatients," Omega, Elsevier, vol. 105(C).
    20. Zhi Chen & Melvyn Sim & Peng Xiong, 2020. "Robust Stochastic Optimization Made Easy with RSOME," Management Science, INFORMS, vol. 66(8), pages 3329-3339, August.

    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:ejores:v:318:y:2024:i:1:p:253-268. 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/eor .

    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.