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Optimal distribution (re)planning in a centralized multi-stage supply network under conditions of the ripple effect and structure dynamics

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  • Ivanov, Dmitry
  • Pavlov, Alexander
  • Sokolov, Boris

Abstract

In this paper, an original approach to formulate and solve a multi-period and multi-commodity distribution (re)planning problem for a multi-stage centralized upstream network with structure dynamics considerations is proposed. First original idea of this study is description of the supply chain as a non-stationary dynamic system along with a linear programming (LP) model. This allows distribute design and control variables between dynamic and static models. Second original idea is to transit from the classical LP model to maximal flow problem by excluding demand constraint from the LP model. The first contribution of this study is multi-objective problem formulation that opens additional perspectives for decision-making beyond cost-oriented optimization. Second, the maximal flow LP model allows the finding of a feasible solution even for unbalanced supply and demand cases without relaxing hard capacity constraints. Third, this allows improve service level at the strategic inventory holding point. Fourth, structure dynamics and ripple effect can be taken into account. Structure dynamics allows considering different execution scenarios and developing suggestions on replanning in the case of disturbances. The graph of structural reliability allows identify the optimistic and pessimistic scenarios. These scenarios are used for computational experiments with the developed model and the industrial models. With the developed model, the practical issues of scenario-based risk identification strategy and operational distribution planning can be interlinked.

Suggested Citation

  • Ivanov, Dmitry & Pavlov, Alexander & Sokolov, Boris, 2014. "Optimal distribution (re)planning in a centralized multi-stage supply network under conditions of the ripple effect and structure dynamics," European Journal of Operational Research, Elsevier, vol. 237(2), pages 758-770.
    • Handle: RePEc:eee:ejores:v:237:y:2014:i:2:p:758-770
    DOI: 10.1016/j.ejor.2014.02.023
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    Cited by:

    1. Dmitry Ivanov & Richard Hartl & Alexandre Dolgui & Alexander Pavlov & Boris Sokolov, 2015. "Integration of aggregate distribution and dynamic transportation planning in a supply chain with capacity disruptions and the ripple effect consideration," International Journal of Production Research, Taylor & Francis Journals, vol. 53(23), pages 6963-6979, December.
    2. Ivanov, Dmitry & Pavlov, Alexander & Pavlov, Dmitry & Sokolov, Boris, 2017. "Minimization of disruption-related return flows in the supply chain," International Journal of Production Economics, Elsevier, vol. 183(PB), pages 503-513.
    3. Yılmaz, Ömer Faruk & Yeni, Fatma Betül & Gürsoy Yılmaz, Beren & Özçelik, Gökhan, 2023. "An optimization-based methodology equipped with lean tools to strengthen medical supply chain resilience during a pandemic: A case study from Turkey," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 173(C).
    4. Dmitry Ivanov, 2017. "Simulation-based ripple effect modelling in the supply chain," International Journal of Production Research, Taylor & Francis Journals, vol. 55(7), pages 2083-2101, April.
    5. Chatterjee, Abheek & Layton, Astrid, 2020. "Mimicking nature for resilient resource and infrastructure network design," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    6. Dmitry Ivanov & Boris Sokolov & Inna Solovyeva & Alexandre Dolgui & Ferry Jie, 2016. "Dynamic recovery policies for time-critical supply chains under conditions of ripple effect," International Journal of Production Research, Taylor & Francis Journals, vol. 54(23), pages 7245-7258, December.
    7. Liu, Ming & Liu, Zhongzheng & Chu, Feng & Dolgui, Alexandre & Chu, Chengbin & Zheng, Feifeng, 2022. "An optimization approach for multi-echelon supply chain viability with disruption risk minimization," Omega, Elsevier, vol. 112(C).
    8. Sinha, Priyank & Kumar, Sameer & Prakash, Surya, 2020. "Measuring and mitigating the effects of cost disturbance propagation in multi-echelon apparel supply chains," European Journal of Operational Research, Elsevier, vol. 282(1), pages 148-160.
    9. Alexander Pavlov & Dmitry Ivanov & Frank Werner & Alexandre Dolgui & Boris Sokolov, 2022. "Integrated detection of disruption scenarios, the ripple effect dispersal and recovery paths in supply chains," Annals of Operations Research, Springer, vol. 319(1), pages 609-631, December.
    10. Ivanov, Dmitry & Dolgui, Alexandre, 2021. "OR-methods for coping with the ripple effect in supply chains during COVID-19 pandemic: Managerial insights and research implications," International Journal of Production Economics, Elsevier, vol. 232(C).
    11. Dmitry Ivanov & Maxim Rozhkov, 2020. "Coordination of production and ordering policies under capacity disruption and product write-off risk: an analytical study with real-data based simulations of a fast moving consumer goods company," Annals of Operations Research, Springer, vol. 291(1), pages 387-407, August.
    12. Sanjoy Kumar Paul & Ruhul Sarker & Daryl Essam & Paul Tae-Woo Lee, 2019. "A mathematical modelling approach for managing sudden disturbances in a three-tier manufacturing supply chain," Annals of Operations Research, Springer, vol. 280(1), pages 299-335, September.
    13. Dmitry Ivanov & Alexandre Dolgui & Boris Sokolov & Marina Ivanova, 2017. "Literature review on disruption recovery in the supply chain," International Journal of Production Research, Taylor & Francis Journals, vol. 55(20), pages 6158-6174, October.
    14. Shraddha Mishra & Surya Prakash Singh, 2022. "Designing dynamic reverse logistics network for post-sale service," Annals of Operations Research, Springer, vol. 310(1), pages 89-118, March.
    15. Chih-Hung Hsu & Xu He & Ting-Yi Zhang & An-Yuan Chang & Wan-Ling Liu & Zhi-Qiang Lin, 2022. "Enhancing Supply Chain Agility with Industry 4.0 Enablers to Mitigate Ripple Effects Based on Integrated QFD-MCDM: An Empirical Study of New Energy Materials Manufacturers," Mathematics, MDPI, vol. 10(10), pages 1-35, May.
    16. Spiegler, Virginia L.M. & Naim, Mohamed M., 2017. "Investigating sustained oscillations in nonlinear production and inventory control models," European Journal of Operational Research, Elsevier, vol. 261(2), pages 572-583.
    17. Ivanov, Dmitry & Pavlov, Alexander & Dolgui, Alexandre & Pavlov, Dmitry & Sokolov, Boris, 2016. "Disruption-driven supply chain (re)-planning and performance impact assessment with consideration of pro-active and recovery policies," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 90(C), pages 7-24.
    18. Dai, B. & Chen, H.X. & Li, Y.A. & Zhang, Y.D. & Wang, X.Q. & Deng, Y.M., 2021. "Inventory replenishment planning of a distribution system with storage capacity constraints and multi-channel order fulfilment," Omega, Elsevier, vol. 102(C).
    19. 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.
    20. Sayan Chakraborty & Akshat Jain & S. P. Sarmah, 2022. "An integrated mathematical model based on grey optimal ranking for supplier selection considering pandemic situation," OPSEARCH, Springer;Operational Research Society of India, vol. 59(4), pages 1613-1648, December.
    21. K. Katsaliaki & P. Galetsi & S. Kumar, 2022. "Supply chain disruptions and resilience: a major review and future research agenda," Annals of Operations Research, Springer, vol. 319(1), pages 965-1002, December.
    22. Rezapour, Shabnam & Farahani, Reza Zanjirani & Pourakbar, Morteza, 2017. "Resilient supply chain network design under competition: A case study," European Journal of Operational Research, Elsevier, vol. 259(3), pages 1017-1035.

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