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An oracle-based algorithm for robust planning of production routing problems in closed-loop supply chains of beverage glass bottles

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  • Borumand, Ali
  • Marandi, Ahmadreza
  • Nookabadi, Ali S.
  • Atan, Zümbül

Abstract

The demand for glass bottles is exhibiting an upward trend over time. The manufacturing of glass bottles is costlier in terms of time and resources and is associated with a higher level of heat generation and environmental pollution compared to recycling processes. In response to the aforementioned challenges, companies that use glass bottles need to implement strategies to manage their reverse supply chains in conjunction with their traditional supply chains, as the economic and environmental benefits of returned products are unquestionable. Closed-loop supply chains (CLSCs) integrate forward and reverse flows of products and information. This integration helps companies to have a broader view of the whole chain. Despite these advantages, managing CLSCs can be challenging as they are exposed to many uncertainties regarding supply and demand processes, travel times, and quantity/quality of returned products.

Suggested Citation

  • Borumand, Ali & Marandi, Ahmadreza & Nookabadi, Ali S. & Atan, Zümbül, 2024. "An oracle-based algorithm for robust planning of production routing problems in closed-loop supply chains of beverage glass bottles," Omega, Elsevier, vol. 122(C).
  • Handle: RePEc:eee:jomega:v:122:y:2024:i:c:s0305048323001032
    DOI: 10.1016/j.omega.2023.102939
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    References listed on IDEAS

    as
    1. Zhi Chen & Melvyn Sim & Peng Xiong, 2020. "Robust Stochastic Optimization Made Easy with RSOME," Management Science, INFORMS, vol. 66(8), pages 3329-3339, August.
    2. Phebe Vayanos & Qing Jin & George Elissaios, 2022. "ROC++: Robust Optimization in C++," INFORMS Journal on Computing, INFORMS, vol. 34(6), pages 2873-2888, November.
    3. Zhalechian, M. & Tavakkoli-Moghaddam, R. & Zahiri, B. & Mohammadi, M., 2016. "Sustainable design of a closed-loop location-routing-inventory supply chain network under mixed uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 89(C), pages 182-214.
    4. Stacey, Nicholas & Edoka, Ijeoma & Hofman, Karen & Swart, Elizabeth C & Popkin, Barry & Ng, Shu Wen, 2021. "Changes in beverage purchases following the announcement and implementation of South Africa's Health Promotion Levy: an observational study," LSE Research Online Documents on Economics 109878, London School of Economics and Political Science, LSE Library.
    5. Chrysanthos E. Gounaris & Wolfram Wiesemann & Christodoulos A. Floudas, 2013. "The Robust Capacitated Vehicle Routing Problem Under Demand Uncertainty," Operations Research, INFORMS, vol. 61(3), pages 677-693, June.
    6. Iassinovskaia, Galina & Limbourg, Sabine & Riane, Fouad, 2017. "The inventory-routing problem of returnable transport items with time windows and simultaneous pickup and delivery in closed-loop supply chains," International Journal of Production Economics, Elsevier, vol. 183(PB), pages 570-582.
    7. Matthews, Logan R. & Gounaris, Chrysanthos E. & Kevrekidis, Ioannis G., 2019. "Designing networks with resiliency to edge failures using two-stage robust optimization," European Journal of Operational Research, Elsevier, vol. 279(3), pages 704-720.
    8. Ben-Tal, Aharon & Chung, Byung Do & Mandala, Supreet Reddy & Yao, Tao, 2011. "Robust optimization for emergency logistics planning: Risk mitigation in humanitarian relief supply chains," Transportation Research Part B: Methodological, Elsevier, vol. 45(8), pages 1177-1189, September.
    9. Amir Ardestani-Jaafari & Erick Delage, 2018. "The Value of Flexibility in Robust Location–Transportation Problems," Transportation Science, INFORMS, vol. 52(1), pages 189-209, January.
    10. Krzysztof Postek & Dick den Hertog, 2016. "Multistage Adjustable Robust Mixed-Integer Optimization via Iterative Splitting of the Uncertainty Set," INFORMS Journal on Computing, INFORMS, vol. 28(3), pages 553-574, August.
    11. Shuai Deng & Yanhui Li & Hao Guo & Bailing Liu, 2016. "Solving a Closed-Loop Location-Inventory-Routing Problem with Mixed Quality Defects Returns in E-Commerce by Hybrid Ant Colony Optimization Algorithm," Discrete Dynamics in Nature and Society, Hindawi, vol. 2016, pages 1-12, June.
    12. Polo, Andrés & Peña, Numar & Muñoz, Dairo & Cañón, Adrián & Escobar, John Willmer, 2019. "Robust design of a closed-loop supply chain under uncertainty conditions integrating financial criteria," Omega, Elsevier, vol. 88(C), pages 110-132.
    13. Zhou, Yu & Xiong, Yu & Jin, Minyue, 2021. "Less is more: Consumer education in a closed-loop supply chain with remanufacturing," Omega, Elsevier, vol. 101(C).
    14. Omar El Housni & Vineet Goyal, 2021. "On the Optimality of Affine Policies for Budgeted Uncertainty Sets," Mathematics of Operations Research, INFORMS, vol. 46(2), pages 674-711, May.
    15. Yipei Zhang & Ada Che & Feng Chu, 2022. "Improved model and efficient method for bi-objective closed-loop food supply chain problem with returnable transport items," International Journal of Production Research, Taylor & Francis Journals, vol. 60(3), pages 1051-1068, February.
    16. David Simchi-Levi & Nikolaos Trichakis & Peter Yun Zhang, 2019. "Designing Response Supply Chain Against Bioattacks," Operations Research, INFORMS, vol. 67(5), pages 1246-1268, September.
    17. Yanıkoğlu, İhsan & Gorissen, Bram L. & den Hertog, Dick, 2019. "A survey of adjustable robust optimization," European Journal of Operational Research, Elsevier, vol. 277(3), pages 799-813.
    18. Ali Pedram & Shahryar Sorooshian & Freselam Mulubrhan & Afshin Abbaspour, 2023. "Incorporating Vehicle-Routing Problems into a Closed-Loop Supply Chain Network Using a Mixed-Integer Linear-Programming Model," Sustainability, MDPI, vol. 15(4), pages 1-24, February.
    19. Dominguez, Roberto & Cannella, Salvatore & Framinan, Jose M., 2021. "Remanufacturing configuration in complex supply chains," Omega, Elsevier, vol. 101(C).
    20. Angelos Georghiou & Angelos Tsoukalas & Wolfram Wiesemann, 2020. "A Primal–Dual Lifting Scheme for Two-Stage Robust Optimization," Operations Research, INFORMS, vol. 68(2), pages 572-590, March.
    21. Postek, Krzysztof & den Hertog, Dick & Kind, Jarl & Pustjens, Chris, 2019. "Adjustable robust strategies for flood protection," Omega, Elsevier, vol. 82(C), pages 142-154.
    22. Dimitris Bertsimas & Iain Dunning, 2016. "Multistage Robust Mixed-Integer Optimization with Adaptive Partitions," Operations Research, INFORMS, vol. 64(4), pages 980-998, August.
    23. Cheng, Chun & Adulyasak, Yossiri & Rousseau, Louis-Martin, 2021. "Robust facility location under demand uncertainty and facility disruptions," Omega, Elsevier, vol. 103(C).
    24. Grani A. Hanasusanto & Daniel Kuhn & Wolfram Wiesemann, 2015. "K -Adaptability in Two-Stage Robust Binary Programming," Operations Research, INFORMS, vol. 63(4), pages 877-891, August.
    25. De, Manoranjan & Giri, B.C., 2020. "Modelling a closed-loop supply chain with a heterogeneous fleet under carbon emission reduction policy," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 133(C).
    26. Arabi, Mahsa & Gholamian, Mohammad Reza, 2023. "Resilient closed-loop supply chain network design considering quality uncertainty: A case study of stone quarries," Resources Policy, Elsevier, vol. 80(C).
    27. A. Ben-Tal & A. Nemirovski, 1998. "Robust Convex Optimization," Mathematics of Operations Research, INFORMS, vol. 23(4), pages 769-805, November.
    28. Liao, Haolan & Zhang, Qingyu & Shen, Neng & Nie, Yongyou & Li, Lu, 2021. "Coordination between forward and reverse production streams for maximum profitability," Omega, Elsevier, vol. 104(C).
    29. Ahmed Timoumi & Narendra Singh & Subodha Kumar, 2021. "Is Your Retailer a Friend or Foe: When Should the Manufacturer Allow Its Retailer to Refurbish?," Production and Operations Management, Production and Operations Management Society, vol. 30(9), pages 2814-2839, September.
    30. M A Colchero & Carlos Manuel Guerrero-López & Mariana Molina & Juan Angel Rivera, 2016. "Beverages Sales in Mexico before and after Implementation of a Sugar Sweetened Beverage Tax," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-8, September.
    31. Galina Iassinovskaia & Sabine Limbourg & Fouad Riane, 2017. "The inventory-routing problem of returnable transport items with time windows and simultaneous pickup and delivery in closed-loop supply chains," Post-Print hal-04333507, HAL.
    32. Govindan, Kannan & Salehian, Farhad & Kian, Hadi & Hosseini, Seyed Teimoor & Mina, Hassan, 2023. "A location-inventory-routing problem to design a circular closed-loop supply chain network with carbon tax policy for achieving circular economy: An augmented epsilon-constraint approach," International Journal of Production Economics, Elsevier, vol. 257(C).
    33. Ward Romeijnders & Krzysztof Postek, 2021. "Piecewise Constant Decision Rules via Branch-and-Bound Based Scenario Detection for Integer Adjustable Robust Optimization," INFORMS Journal on Computing, INFORMS, vol. 33(1), pages 390-400, January.
    34. Qiu, Yuzhuo & Ni, Ming & Wang, Liang & Li, Qinqin & Fang, Xuanjing & Pardalos, Panos M., 2018. "Production routing problems with reverse logistics and remanufacturing," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 111(C), pages 87-100.
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