A Bi-Objective Optimization Model for a Low-Carbon Supply Chain Network with Risk of Uncertain Disruptions

Disruption risks exacerbate the complexity of low-carbon supply chain network design in an uncertain supply chain environment. Considering the low frequency and non-repeatability of these disruption events makes it impossible to collect data to obtain their probabilities. In this study, supply disru...

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Bibliographic Details
Published in:Symmetry (Basel) 2023-09, Vol.15 (9), p.1707
Main Authors: Wang, Yingtong, Ji, Xiaoyu, Lang, Yutong
Format: Article
Language:English
Subjects:
Algorithms
Carbon
Carbon content
Case studies
Component and supplier management
Costs
COVID-19
Decision making
Design
Disruption
Emissions
Emissions (Pollution)
Glass industry
Integer programming
International economic relations
Investment analysis
low-carbon supply chain
Network design
Network management systems
network optimization
Objectives
Optimization
Optimization models
Suppliers
Supply chains
uncertain disruption risk
uncertainty theory
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Summary:Disruption risks exacerbate the complexity of low-carbon supply chain network design in an uncertain supply chain environment. Considering the low frequency and non-repeatability of these disruption events makes it impossible to collect data to obtain their probabilities. In this study, supply disruptions were regarded as uncertain events; supply chain uncertain disruption risk is defined and quantified based on the uncertainty theory, in which uncertain disruptions are characterized by the belief degree on account of expert estimation with duality, i.e., symmetry. Optimization models were constructed with the objective of minimizing expected carbon emissions and costs, which optimizes the selection of suppliers with uncertain disruptions, and the assignment of manufacturers and customers. The properties of the model were analyzed, and the models were solved separately using different methods according to different decision criteria. Finally, the validity of the proposed models and algorithm were verified using a real case study of a glass manufacturing company. The findings exhibit promising insights for designing a sustainable and resilient supply chain network in an uncertain environment.
ISSN:2073-8994
2073-8994
DOI:10.3390/sym15091707