Stochastic Integrated Supplier Selection and Disruption Risk Assessment Under Ripple Effect

Ming Liu (1) , Zhongzheng Liu (1) , Feng Chu (2) , Feifeng Zheng (3) , Chengbin Chu (4)

1 School of Economics and Management
2 IBISC - Informatique, BioInformatique, Systèmes Complexes
3 Glorious Sun School of Business & Management
4 LIGM - Laboratoire d'Informatique Gaspard-Monge

The impact of the COVID-19 pandemic in the supply chain (SC) evokes the need for valid measures to cope with the SC disruption risk. Supplier selection and disruption risk assessment, as valid measures, have received increasing attentions from academia. However, most of existing works focus on supplier selection and disruption risk assessment separately. This work investigates an integrated supplier selection and disruption risk assessment problem under ripple effect. The objective is to minimize the weighted sum of the disrupted probability and the total cost for the manufacturer. For the problem, a new stochastic programming model combined with Bayesian network (BN) is formulated. Then, an illustrative example is conducted to demonstrate the proposed method.

Keywords: Bayesian network; Disruption risk assessment; Stochastic programming model; Supplier selection; Supply chain disruption risk

Domains: Operations Research [cs.RO]

Ming Liu, Zhongzheng Liu, Feng Chu, Feifeng Zheng, Chengbin Chu. Stochastic Integrated Supplier Selection and Disruption Risk Assessment Under Ripple Effect. International Conference on Advances in Production Management Systems (APMS 2021), Sep 2021, Nantes, France. pp.689--696, ⟨10.1007/978-3-030-85906-0_75⟩. ⟨hal-03360839⟩