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Resilience-Based Restoration Model for Supply Chain Networks

Author

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  • Xinhua Mao

    (School of Economics and Management, Chang’an University, Xi’an 710064, China
    Engineering Research Center of Highway Infrastructure Digitalization, Ministry of Education, Chang’an University, Xi’an 710064, China
    Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada)

  • Xin Lou

    (Road Transport Development Center of Shaanxi Province, Xi’an 710003, China)

  • Changwei Yuan

    (School of Economics and Management, Chang’an University, Xi’an 710064, China
    Engineering Research Center of Highway Infrastructure Digitalization, Ministry of Education, Chang’an University, Xi’an 710064, China)

  • Jibiao Zhou

    (College of Transportation Engineering, Tongji University, Shanghai 200082, China)

Abstract

An optimal restoration strategy for supply chain networks can efficiently schedule the repair activities under resource limits. However, a wide range of previous studies solve this problem from the perspective of cost-effectiveness instead of a resilient manner. This research formulates the problem as a network maximum-resilience decision. We develop two metrics to measure the resilience of the supply chain networks, i.e., the resilience of cumulative performance loss and the resilience of restoration rapidity. Then, we propose a bi-objective nonlinear programming model, which aims to maximize the network resilience under the budget and manpower constraints. A modified simulated annealing algorithm is employed to solve the model. Finally, a testing supply chain network is utilized to illustrate the effectiveness of the proposed method framework. The results show that the optimal restoration schedule generated by the proposed model is a tradeoff between the cumulative performance loss and the restoration rapidity. Additionally, the sensitivity analysis of parameters indicates that decision-maker’s preference, tolerance factor of delivery time, number of work crews, and availability of budget all have significant impacts on the restoration schedule.

Suggested Citation

  • Xinhua Mao & Xin Lou & Changwei Yuan & Jibiao Zhou, 2020. "Resilience-Based Restoration Model for Supply Chain Networks," Mathematics, MDPI, vol. 8(2), pages 1-16, January.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:2:p:163-:d:312325
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    References listed on IDEAS

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    1. Ouyang, Min & Wang, Zhenghua, 2015. "Resilience assessment of interdependent infrastructure systems: With a focus on joint restoration modeling and analysis," Reliability Engineering and System Safety, Elsevier, vol. 141(C), pages 74-82.
    2. Wang, Shaojun & Sarker, Bhaba R. & Mann, Lawrence & Triantaphyllou, Evangelos, 2004. "Resource planning and a depot location model for electric power restoration," European Journal of Operational Research, Elsevier, vol. 155(1), pages 22-43, May.
    3. Kasin Ransikarbum & Scott J. Mason, 2016. "Multiple-objective analysis of integrated relief supply and network restoration in humanitarian logistics operations," International Journal of Production Research, Taylor & Francis Journals, vol. 54(1), pages 49-68, January.
    4. Endong Zhu & Teodor Gabriel Crainic & Michel Gendreau, 2014. "Scheduled Service Network Design for Freight Rail Transportation," Operations Research, INFORMS, vol. 62(2), pages 383-400, April.
    5. Almoghathawi, Yasser & Barker, Kash & Albert, Laura A., 2019. "Resilience-driven restoration model for interdependent infrastructure networks," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 12-23.
    6. Prabuddha De & E. James Dunne & Jay B. Ghosh & Charles E. Wells, 1997. "Complexity of the Discrete Time-Cost Tradeoff Problem for Project Networks," Operations Research, INFORMS, vol. 45(2), pages 302-306, April.
    7. Mohamad Darayi & Kash Barker & Joost R. Santos, 2017. "Component Importance Measures for Multi-Industry Vulnerability of a Freight Transportation Network," Networks and Spatial Economics, Springer, vol. 17(4), pages 1111-1136, December.
    8. Nurre, Sarah G. & Cavdaroglu, Burak & Mitchell, John E. & Sharkey, Thomas C. & Wallace, William A., 2012. "Restoring infrastructure systems: An integrated network design and scheduling (INDS) problem," European Journal of Operational Research, Elsevier, vol. 223(3), pages 794-806.
    9. Henry, Devanandham & Emmanuel Ramirez-Marquez, Jose, 2012. "Generic metrics and quantitative approaches for system resilience as a function of time," Reliability Engineering and System Safety, Elsevier, vol. 99(C), pages 114-122.
    10. Sanci, Ece & Daskin, Mark S., 2019. "Integrating location and network restoration decisions in relief networks under uncertainty," European Journal of Operational Research, Elsevier, vol. 279(2), pages 335-350.
    11. Timothy Matisziw & Alan Murray & Tony Grubesic, 2010. "Strategic Network Restoration," Networks and Spatial Economics, Springer, vol. 10(3), pages 345-361, September.
    12. Sungbin Cho & Peter Gordon & Harry W. Richardson & James E. Moore & Masanobu Shinozuka, 2000. "Analyzing Transportation Reconstruction Network Strategies: A Full Cost Approach," Review of Urban & Regional Development Studies, Wiley Blackwell, vol. 12(3), pages 212-227, November.
    13. Fang, Yi-Ping & Sansavini, Giovanni, 2019. "Optimum post-disruption restoration under uncertainty for enhancing critical infrastructure resilience," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 1-11.
    14. Lichun Chen & Elise Miller-Hooks, 2012. "Resilience: An Indicator of Recovery Capability in Intermodal Freight Transport," Transportation Science, INFORMS, vol. 46(1), pages 109-123, February.
    15. L. Ingber, 1993. "Simulated annealing: Practice versus theory," Lester Ingber Papers 93sa, Lester Ingber.
    16. Crainic, Teodor Gabriel, 2000. "Service network design in freight transportation," European Journal of Operational Research, Elsevier, vol. 122(2), pages 272-288, April.
    17. SteadieSeifi, M. & Dellaert, N.P. & Nuijten, W. & Van Woensel, T. & Raoufi, R., 2014. "Multimodal freight transportation planning: A literature review," European Journal of Operational Research, Elsevier, vol. 233(1), pages 1-15.
    18. Li, Zhaolong & Jin, Chun & Hu, Pan & Wang, Cong, 2019. "Resilience-based transportation network recovery strategy during emergency recovery phase under uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 503-514.
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    Cited by:

    1. Gast, Johannes & Kirkach, Evelina & Ivanov, Dmitry, 2022. "Structured literature review of transport networks and Supply Chain Resilience," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Jahn, Carlos & Blecker, Thorsten & Ringle, Christian M. (ed.), Changing Tides: The New Role of Resilience and Sustainability in Logistics and Supply Chain Management – Innovative Approaches for the Shift to a New , volume 33, pages 469-496, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    2. Syed Imran Zaman & Sharfuddin Ahmed Khan & Sahar Qabool & Himanshu Gupta, 2023. "How digitalization in banking improve service supply chain resilience of e-commerce sector? a technological adoption model approach," Operations Management Research, Springer, vol. 16(2), pages 904-930, June.
    3. Hongyan Dui & Zhe Xu & Liwei Chen & Liudong Xing & Bin Liu, 2022. "Data-Driven Maintenance Priority and Resilience Evaluation of Performance Loss in a Main Coolant System," Mathematics, MDPI, vol. 10(4), pages 1-18, February.
    4. Hongyan Dui & Huiting Xu & Yun-An Zhang, 2022. "Reliability Analysis and Redundancy Optimization of a Command Post Phased-Mission System," Mathematics, MDPI, vol. 10(22), pages 1-15, November.
    5. Hosseini, Yaser & Mohammadi, Reza Karami & Yang, Tony Y., 2024. "A comprehensive approach in post-earthquake blockage prediction of urban road network and emergency resilience optimization," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    6. Safinaz H. Abourokbah & Reem M. Mashat & Mohammad Asif Salam, 2023. "Role of Absorptive Capacity, Digital Capability, Agility, and Resilience in Supply Chain Innovation Performance," Sustainability, MDPI, vol. 15(4), pages 1-25, February.

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