Supply Chain Resilience and Resilient Logistics Management

Dear Colleagues,

In recent years, supply chains (SCs) have been more susceptible to a variety of disruptions, including natural disasters, human-made attacks, and common failures, because of the complexity of their structures, globalization issues, and the frequency of disruptions. Hence, many businesses are increasingly recognizing the importance of designing resilient and reliable SCs, with a high capability of withstanding and recovering rapidly from disruptive events. As the disturbances could affect the normal operations of an SC, resulting in delivery delays, loss of market share and reputation, and poor customer satisfaction, a resilient supply chain is critical to the success of an enterprise. After the Japanese Tsunami and earthquake in 2011, many automotive and electronics companies tried to practice resilient strategies for their SC and logistics systems. The concept of resilience in SCs is relatively new when compared with green and sustainability concepts. Several efforts have been devoted to define supply chain resilience (SCR) or to develop pre-disaster and post-disaster strategies in order to achieve a high level of resilience. However, there are several key questions remaining unanswered, namely: (i) how to quantify the resilience of SCs with respect to single and multiple disruptions, (ii) how to improve the resilience of SCs, (iii) how to simulate and measure the impact of disruption occurring upstream of SC on downstream entities, and (iv) how to identify the SC entities whose performances have had the highest impact of SC resilience and performance.  

The purpose of this Special Issue is to fill the aforementioned gaps by focusing on measuring, analyzing, and improving the resilience of SCs and logistics systems.

Potential topics include, but are not limited to, the following:

• Evaluating resilient supplier and vendor selection problems
• Measuring the resilience of SC and logistics systems
• Resilience cost investment via pre-disruption and post-disruption activities
• Analyzing the impact of the SC structure on achieving a high-resilience level
• Recovery and repair strategies on resilient supply networks
• Developing quantitative models using quantitative methodologies (e.g., optimization, multi-criteria decision making methods, game theory, and simulation) to model resilient SCs and logistics

This Special Issue is open to various research methodologies, including qualitative (e.g., empirical studies) and quantitative (e.g., mathematical modeling, simulation, game theory, neural network, data mining, Bayesian network, machine learning, regression analysis, structural equations modeling, and data-driven analytics). The study can adopt any methodology that fits the research question. Some recent studies on SCR can be found in the bibliography of this call for papers.

References

1. Hosseini, S., Ivanov, D., Dolgui, A. (2019). Review of quantitative methods for supply chain resilience analysis. Transportation Research Part E, 125: 285-307.
2. Hosseini, S., Barker, K. (2016). A Bayesian network model for resilience-based supplier selection. International Journal of Production Economics, 180: 68-87.
3. Hosseini, S., Barker, K. (2016). Modeling infrastructure resilience using Bayesian networks: A case study of inland waterway ports. Computers & Industrial Engineering, 93: 252-266.
4. Hosseini, S., Al Khaled, A. (2019). A hybrid ensemble and AHP approach for resilient supplier selection. Journal of Intelligent Manufacturing, 30(1): 207-228.
5. Lopez, C., Ishizaka, A. (2019). A hybrid FCM-AHP approach to predict impact of offshore outstanding location decision on supply chain resilience. Journal of Business Research, 103: 495-507.
6. Hosseini, S., Morshedlou, N., Ivanov, D., Sarder, MD, Barker, K. (2019). Resilient supplier selection and optimal order allocation under disruption risks. International Journal of Production Economics, 213: 124-137.
7. Jun Tan, W., Zhang, A.N., Cai, W. (2019). A graph-based model to measure structural redundancy for supply chain resilience. International Journal of Production Research.
8. Ivanov, D. (2017). Revealing interfaces of supply chain resilience and sustainability: a simulation study. International Journal of Production Research, 56(10): 3507-3523.
9. Hosseini, S., Ivanov, D., Dolgui, A. (2019). Ripple effect modeling of supplier disruption: integrated Markov chain and dynamic Bayesian network approach. International Journal of Production Research.

Deadline: April 30, 2020

Dr. Mohsen Hosseini
Guest Editor

Manuscript Submission Information

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• Supply chain resilience
• resilient supplier selection
• supply disruption
• supply chain risk
• resilient logistics
• resilient supply chain design
• logistics disruption