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Integrated production–distribution planning in two-echelon systems: a resilience view

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  • Seyed Mohammad Khalili
  • Fariborz Jolai
  • Seyed Ali Torabi

Abstract

Global supply chains are increasingly exposed to operational and disruption risks that threaten their business continuity. This paper presents a novel two-stage scenario-based mixed stochastic-possibilistic programming model for integrated production and distribution planning problem in a two-echelon supply chain over a midterm horizon under risk. Operational risks are handled by introducing imprecise (i.e. possibilistic) parameters while disruption risks are accounted for through stochastic disruption scenarios. The proposed model accounts for the risk mitigation options and recovery of lost capacities in an integrated manner. In the first stage, the structure of the chain and proactive risk mitigation decisions are determined, while the second stage specifies the recovery plan of lost capacities in addition to production and distribution plans. Considering extra capacities in the production facilities, backup routes for transportation links and pre-positioning of emergency inventory in distribution centres are considered as feasible options to improve the resilience level of the supply chain. We propose a new indicator for optimising the resilience level of the chain based on restoration of lost capacities. For the sake of robustness, the expected worst case of the second stage’s objective function is considered by utilising the conditional value at risk (CVaR) measure. The validation and applicability of the proposed model are examined through several numerical experiments.

Suggested Citation

  • Seyed Mohammad Khalili & Fariborz Jolai & Seyed Ali Torabi, 2017. "Integrated production–distribution planning in two-echelon systems: a resilience view," International Journal of Production Research, Taylor & Francis Journals, vol. 55(4), pages 1040-1064, February.
    • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:4:p:1040-1064
    DOI: 10.1080/00207543.2016.1213446
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    Cited by:

    1. Shoufeng Ji & Pengyun Zhao & Tingting Ji, 2023. "A Hybrid Optimization Method for Sustainable and Flexible Design of Supply–Production–Distribution Network in the Physical Internet," Sustainability, MDPI, vol. 15(7), pages 1-34, April.
    2. Nayeri, Sina & Sazvar, Zeinab & Heydari, Jafar, 2022. "A global-responsive supply chain considering sustainability and resiliency: Application in the medical devices industry," Socio-Economic Planning Sciences, Elsevier, vol. 82(PB).
    3. Dixit, Vijaya & Verma, Priyanka & Tiwari, Manoj Kumar, 2020. "Assessment of pre and post-disaster supply chain resilience based on network structural parameters with CVaR as a risk measure," International Journal of Production Economics, Elsevier, vol. 227(C).
    4. Martha-Selene Casas-Ramírez & José-Fernando Camacho-Vallejo & Rosa G. González-Ramírez & José-Antonio Marmolejo-Saucedo & José-Manuel Velarde-Cantú, 2018. "Optimizing a Biobjective Production-Distribution Planning Problem Using a GRASP," Complexity, Hindawi, vol. 2018, pages 1-13, February.
    5. Hrabec, Dušan & Hvattum, Lars Magnus & Hoff, Arild, 2022. "The value of integrated planning for production, inventory, and routing decisions: A systematic review and meta-analysis," International Journal of Production Economics, Elsevier, vol. 248(C).
    6. Hosseini, Seyedmohsen & Ivanov, Dmitry & Dolgui, Alexandre, 2019. "Review of quantitative methods for supply chain resilience analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 125(C), pages 285-307.
    7. Chen, Li-Ming & Chang, Wei-Lun, 2021. "Supply- and cyber-related disruptions in cloud supply chain firms: Determining the best recovery speeds," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 151(C).
    8. Cheramin, Meysam & Saha, Apurba Kumar & Cheng, Jianqiang & Paul, Sanjoy Kumar & Jin, Hongyue, 2021. "Resilient NdFeB magnet recycling under the impacts of COVID-19 pandemic: Stochastic programming and Benders decomposition," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 155(C).
    9. Maureen S. Golan & Laura H. Jernegan & Igor Linkov, 2020. "Trends and applications of resilience analytics in supply chain modeling: systematic literature review in the context of the COVID-19 pandemic," Environment Systems and Decisions, Springer, vol. 40(2), pages 222-243, June.
    10. Changchun Liu & Xi Xiang & Li Zheng, 2020. "Value of information sharing in a multiple producers–distributor supply chain," Annals of Operations Research, Springer, vol. 285(1), pages 121-148, February.
    11. Mahyar Mirabnejad & Hadi Mohammadi & Mehrdad Mirzabaghi & Amir Aghsami & Fariborz Jolai & Maziar Yazdani, 2022. "Home Health Care Problem with Synchronization Visits and Considering Samples Transferring Time: A Case Study in Tehran, Iran," IJERPH, MDPI, vol. 19(22), pages 1-25, November.
    12. Olga Lingaitienė & Juozas Merkevičius & Vida Davidavičienė, 2021. "The Model of Vehicle and Route Selection for Energy Saving," Sustainability, MDPI, vol. 13(8), pages 1-20, April.
    13. Sesini, Marzia & Giarola, Sara & Hawkes, Adam D., 2021. "Strategic natural gas storage coordination among EU member states in response to disruption in the trans Austria gas pipeline: A stochastic approach to solidarity," Energy, Elsevier, vol. 235(C).
    14. Iman Kazemian & S. Ali Torabi & Christopher W. Zobel & Yuhong Li & Milad Baghersad, 2022. "A multi-attribute supply chain network resilience assessment framework based on SNA-inspired indicators," Operational Research, Springer, vol. 22(3), pages 1853-1883, July.
    15. Kaur, Harpreet & Prakash Singh, Surya, 2021. "Multi-stage hybrid model for supplier selection and order allocation considering disruption risks and disruptive technologies," International Journal of Production Economics, Elsevier, vol. 231(C).
    16. Aldrighetti, Riccardo & Battini, Daria & Ivanov, Dmitry & Zennaro, Ilenia, 2021. "Costs of resilience and disruptions in supply chain network design models: A review and future research directions," International Journal of Production Economics, Elsevier, vol. 235(C).

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