An optimization approach for sustainable and resilient supply chain design with regional considerations

•Designing a sustainable and resilient supply chain with regional considerations.•Using an elastic p-robustness measure to manage operational risks.•Investigating an actual case study and proposing several managerial insights.•Using a k-means clustering method to assess the regions' sustainabil...

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Bibliographic Details
Published in:Computers & industrial engineering Vol. 159; p. 107510
Main Authors: Sabouhi, Fatemeh, Jabalameli, Mohammad Saeed, Jabbarzadeh, Armin
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.09.2021
Subjects:
K-means clustering method
Regional approach
Resilience
Sustainability
Regional approach
K-means clustering method
Sustainability
Resilience
ISSN:0360-8352, 1879-0550
Online Access:Get full text
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Summary:•Designing a sustainable and resilient supply chain with regional considerations.•Using an elastic p-robustness measure to manage operational risks.•Investigating an actual case study and proposing several managerial insights.•Using a k-means clustering method to assess the regions' sustainability performance.•Developing a Benders decomposition algorithm to solve the model. Regional and geographical differences between facilities is of paramount importance in supply chain design. However, the impact of the regions' performance on supply chain design decisions remains a relatively under-researched subject in the literature. This paper presents a hybrid methodology for designing a sustainable supply chain that is resilient to random disruptions. We propose a multi-period multi-objective optimization model that utilizes a k-means clustering method to evaluate the regions' sustainability performance. The model aims to determine sourcing and network design decisions as well as resilience strategies. To manage the operational risks associated with the supply chain, we employ a new robustness measure that eliminates the need to estimate the probability distribution of random parameters. Finally, a Benders decomposition algorithm is developed to solve the model. Practical insights are drawn from an actual case study of a downstream petrochemical industry in Iran.
ISSN:0360-8352
1879-0550
DOI:10.1016/j.cie.2021.107510