Prediction-based decomposition optimisation for multi-portfolio supply chain resilience strategies under disruption risks

ABSTRACT

This paper focuses on the design of supply chain (SC) risk mitigation and recovery strategies during long-term disruptions caused by the COVID-19 outbreak, which affect both suppliers and plants. Consequently, concurrent disruptions in supply and production are observed, which vary in duration and result in time-varying reductions in supply and production capacity. To cope with long-term disruptions, a modified multi-portfolio approach that integrates simulation and predictions is proposed to develop efficient mitigation and recovery plans. This approach involves selecting primary and recovery supply and production portfolios concurrently. To achieve this objective, time-dependent mixed integer programming (MIP) models that incorporate preparedness and recovery measures are developed to optimise SC operations. A prediction-based decomposition optimisation method is proposed to solve MIP problems and coordinate supply and production portfolios under disruptions and uncertainties. Furthermore, a heuristic approach is established to provide a comprehensive solution process. Finally, computational experiments and comparative analysis are conducted on a real-life case study. The results demonstrate that the proposed modelling and optimisation methods can effectively address disruptions and improve SC resilience. In addition, the developed models and approaches have the potential to serve as decision-making tools in SC management during disruptions.

KEYWORDS:

• Prediction-based decomposition optimisation
• supply and production disruptions
• COVID-19 pandemic
• time-dependent mixed integer programming
• multi-portfolio approach

Acknowledgments

The authors would like to express their sincerest gratitude to the Editor-in-Chief, Associate Editor, and three anonymous reviewers for their invaluable and constructive comments, which have improved this paper immensely.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data is available upon request from Dongsheng Electronics Co., Ltd.

Notes

1 https://www.chinadaily.com.cn/a/202302/17/WS63eed6e7a31057c47ebaf529.html.

2 https://www.163.com/dy/article/HQDNL6H90553VYU3.html.

Additional information

Funding

This work is supported in part by the National Natural Science Foundation of China [grant number 62173218], and the International Corporation Project of Shanghai Science and Technology Commission [grant number 21190780300].

Notes on contributors

Yi Yang

Yi Yang was born in Jiangsu province, China, in 1995. He is currently pursuing the Ph.D. degree in control science and engineering with the School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China. His current research interests include supply chain risk management, supply chain resilience, and product and supply chain system change management.

Chen Peng

Chen Peng received the B.Sc. and M.Sc. degrees in coal preparation, and the Ph.D. degree in control theory and control engineering from the Chinese University of Mining Technology, Xuzhou, China, in 1996, 1999, and 2002, respectively. From November 2004 to January 2005, he was a Research Associate with the University of Hong Kong, Hong Kong. From July 2006 to August 2007, he was a Visiting Scholar with the Queensland University of Technology, Brisbane, QLD, Australia. From July 2011 to August 2012, he was a Postdoctoral Research Fellow with Central Queensland University, Rockhampton, QLD, Australia. In 2012, he was appointed as an Eastern Scholar with the Municipal Commission of Education, Shanghai, China, and joined Shanghai University, Shanghai. His current research interests include networked control systems, distributed control systems, smart grid, and intelligent control systems. Dr. Peng is an Associate Editor of a number of international journals, including the IEEE Transactions on Industrial Informatics, Information Sciences, and Transactions of the Institute of Measurement and Control. He was named a Highly Cited Researcher from 2020 to 2023 by Clarivate Analytics.

En-Zhi Cao

En-Zhi Cao was born in Anhui Province, China, in 1996. He is currently working toward the Ph.D. degree in control science and engineering with the School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China. His current research interests include modelling, quantification, and change control of product and supply chain systems.

Wenxuan Zou

Wenxuan Zou was born in Jiangsu Province, China, in 2000. She is currently pursuing the Master's degree in control science and engineering with the School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China. Her current research interests include modelling, quantification and change control of supply chain systems.