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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

    as
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    Cited by:

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    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. 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.
    4. 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.
    5. 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.

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