Work

Risk-averse Dynamic Bayesian Network for Supply Chain Risk Evaluation under Ripple Effects and Risk Dispersion

Year: 2025

Type: article

Abstract: Severe disruptions in the supply chain (SC) trigger a ripple effect, leading to significant temporal and structural propagation with lasting impacts. Mitigating these effects requires a thorough SC risk assessment, focusing on performance fluctuations to inform long-term crisis strategies. Although prior studies often overlook dispersion characteristics, particularly from a risk-averse perspective, this research introduces a novel approach to evaluate multi-echelon SC risk under data scarcity. The proposed method: (i) models temporal and structural propagation using a risk-averse dynamic Bayesian network (RA-DBN) and probability interval method (PIM); (ii) formulates the problem with a risk-averse nonlinear programming (RA-NLP) model; and (iii) employs a quadratically constrained quadratic program (QCQP) technique for efficient resolution.

Source: IFAC-PapersOnLine

DOI: 10.1016/j.ifacol.2025.09.125

Authors: Ming Liu, Zhongzheng Liu, Feng Chu, Feifeng Zheng, Chengbin Chu

Institutions: Tongji University, Informatique, BioInformatique, Systèmes Complexes, Université d'Évry Val-d'Essonne, Donghua University, ESIEE Paris

Language: English

Topic: Supply Chain Resilience and Risk Management

Subfield: Strategy and Management

Field: Business, Management and Accounting

Domain: Social Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

Funder: National Natural Science Foundation of China