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Minimization of disruption-related return flows in the supply chain

Listed author(s):
  • Ivanov, Dmitry
  • Pavlov, Alexander
  • Pavlov, Dmitry
  • Sokolov, Boris
Registered author(s):

    Recent research on closed-loop supply chains (SC) and reverse logistics extensively emphasises the crucial role of reducing negative return flows such as emissions, waste, etc. In this study, we consider the return flows in the SC in light disruptive events in the SC. The objective of this study is to compare the performance impact of different recovery policies on return flows subject to the simultaneously optimized re-configuration plans for material flows. We formulate a multi-objective problem with return flow reduction function for a multi-period, multi-stage, multi-product SC. We consider a recovery problem with ripple effect, performance impact assessment and re-planning decisions. The developed multi-objective hybrid linear programming-system dynamics model allows simultaneously re-computing the material flows in a multi-stage SC after a disruption and comparing the performance impact of different recovery policies subject to return flows, gradual capacity recovery, variable recovery costs and time. The results suggest that the consideration of gradual capacity recovery leads to minimization of disruption-related return flows in both upstream and downstream SC parts. Fast and expensive recovery strategy provides the lowest return costs in the upstream SC part as compared to normal and slow recovery policies. Similar, the profits and service levels are increased. In the fast and expensive recovery policy, the performance in the upstream and downstream does not change with the introduction of the gradual recovery considerations. The effects of gradual capacity recovery introduction become evident if smaller time sub-periods are considered within the recovery period.

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    Article provided by Elsevier in its journal International Journal of Production Economics.

    Volume (Year): 183 (2017)
    Issue (Month): PB ()
    Pages: 503-513

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    Handle: RePEc:eee:proeco:v:183:y:2017:i:pb:p:503-513
    DOI: 10.1016/j.ijpe.2016.03.012
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    1. Ivanov, Dmitry & Sokolov, Boris, 2013. "Control and system-theoretic identification of the supply chain dynamics domain for planning, analysis and adaptation of performance under uncertainty," European Journal of Operational Research, Elsevier, vol. 224(2), pages 313-323.
    2. 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.
    3. Azaron, A. & Brown, K.N. & Tarim, S.A. & Modarres, M., 2008. "A multi-objective stochastic programming approach for supply chain design considering risk," International Journal of Production Economics, Elsevier, vol. 116(1), pages 129-138, November.
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    6. Das, Kanchan & Chowdhury, Abdul H., 2012. "Designing a reverse logistics network for optimal collection, recovery and quality-based product-mix planning," International Journal of Production Economics, Elsevier, vol. 135(1), pages 209-221.
    7. Cui, Tingting & Ouyang, Yanfeng & Shen, Zuo-Jun Max J, 2010. "Reliable Facility Location Design under the Risk of Disruptions," University of California Transportation Center, Working Papers qt5sh2c7pw, University of California Transportation Center.
    8. Liberatore, Federico & Scaparra, Maria P. & Daskin, Mark S., 2012. "Hedging against disruptions with ripple effects in location analysis," Omega, Elsevier, vol. 40(1), pages 21-30, January.
    9. Gedik, Ridvan & Medal, Hugh & Rainwater, Chase & Pohl, Ed A. & Mason, Scott J., 2014. "Vulnerability assessment and re-routing of freight trains under disruptions: A coal supply chain network application," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 71(C), pages 45-57.
    10. 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.
    11. Sawik, Tadeusz, 2015. "On the fair optimization of cost and customer service level in a supply chain under disruption risks," Omega, Elsevier, vol. 53(C), pages 58-66.
    12. Zhibin (Ben) Yang & Göker Aydın & Volodymyr Babich & Damian R. Beil, 2012. "Using a Dual-Sourcing Option in the Presence of Asymmetric Information About Supplier Reliability: Competition vs. Diversification," Manufacturing & Service Operations Management, INFORMS, vol. 14(2), pages 202-217, April.
    13. Michael K. Lim & Achal Bassamboo & Sunil Chopra & Mark S. Daskin, 2013. "Facility Location Decisions with Random Disruptions and Imperfect Estimation," Manufacturing & Service Operations Management, INFORMS, vol. 15(2), pages 239-249, May.
    14. Losada, Chaya & Scaparra, M. Paola & O’Hanley, Jesse R., 2012. "Optimizing system resilience: A facility protection model with recovery time," European Journal of Operational Research, Elsevier, vol. 217(3), pages 519-530.
    15. Wilson, Martha C., 2007. "The impact of transportation disruptions on supply chain performance," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 43(4), pages 295-320, July.
    16. Sagawa, Juliana Keiko & Nagano, Marcelo Seido, 2015. "Modeling the dynamics of a multi-product manufacturing system: A real case application," European Journal of Operational Research, Elsevier, vol. 244(2), pages 624-636.
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