A new robust dynamic Bayesian network approach for disruption risk assessment under the supply chain ripple effect

Abstract

Dynamic Bayesian network (DBN) theory provides a valid tool to estimate the risk of disruptions, propagating along the supply chain (SC), i.e. the ripple effect. However, in cases of data scarcity, obtaining perfect information on probability distributions required by the DBN is impractical. To overcome this difficulty, a new robust DBN approach is, for the first time, proposed in this study to analyse the worst-case oriented disruption propagation in the SC. This work considers an SC with multiple suppliers and one manufacturer over several time periods, in which only probability intervals of the suppliers' states and those of the related disruption propagations are known. The objective is to acquire the robust performance of risk estimation, measured by the worst-case probability in the disrupted state for the manufacturer. We first establish a nonlinear programming formulation to mathematically materialise the proposed robust DBN, which can be used to solve small-size problems. To overcome the computational difficulty in solving large-size problems, an efficient simulated annealing algorithm is further designed. Numerical experiments are conducted to validate its efficiency.

Keywords:

• Supply chain
• disruption risk management
• data scarcity
• robust dynamic bayesian network
• probability interval

Acknowledgments

The authors are grateful for the valuable comments from the reviewers. This work was supported by the National Natural Science Foundation of China (NSFC) under Grants 72021002, 71771048, 71432007, 71832001 and 72071144.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (NSFC) under Grants 72021002, 71771048, 71432007, 71832001 and 72071144.

Notes on contributors

Ming Liu

Ming Liu, received the B.S. degree in management science and engineering and the Ph.D. degree in management science and engineering from Xi'an Jiaotong University, Xi'an, China, in 2005 and 2010.

He is currently an Associate Professor at Tongji University, Shanghai, China. His research interests include port logistics optimisation and production scheduling.

Zhongzheng Liu

Zhongzheng Liu, received the B.S. degree in automotive engineering from Hefei University of Technology, Hefei, China, in 2018. He is currently working toward the M.S. degree in the Economic and Management school, Tongji University, Shanghai, China. His research interests include machine scheduling.

Feng Chu

Feng Chu, received the B.S. degree in electrical engineering from the Hefei University of Technology, Hefei, China, in 1986, the M.S. degree in metrology, automatic control, and electrical engineering from the National Polytechnic Institute of Lorraine, Lorraine, France, in 1991, and the Ph.D. degree in automatic control, computer science, and production management from the University of Metz, Metz, France, in 1995.

She is currently a Full Professor of operations research with the University of Évry Val dEssonne, the University of Paris-Saclay, Évry, France, and a Co-Leader of the Algorithmic, Operations Research, Bioinformatics and Statistical Learning Group.

Feifeng Zheng

Feifeng Zheng, received the B.S. degree in information management, the M.S. degree in management science and engineering and the Ph.D. degree in management science and engineering from Xi'an Jiaotong University, Xi'an, China, in 1998, 2003 and 2006.

Feifeng Zheng is a Professor at Donghua University, Shanghai. His research interests include production scheduling and container terminal resource scheduling. He is currently member of editorial board for ‘Operations Research and Management Science’ from 2017.

Chengbin Chu

Chengbin Chu, received the B.S. degree in electrical engineering from the Hefei University of Technology, Hefei, China, in 1985, and the Ph.D. degree in computer science from the University of Metz, Metz, France, in 1990.

He was with the National Research Institute in Computer Science and Automation, Metz, from 1987 to 1996. He was a Professor with the University of Technology of Troyes, Troyes, France, from 1996 to 2008, where he was the Founding Director of the Industrial Systems Optimization Laboratory. He is currently a Professor with ESIEE Paris, Universita Paris-Est, France.