Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/287199 
Year of Publication: 
2021
Citation: 
[Journal:] Annals of Operations Research [ISSN:] 1572-9338 [Publisher:] Springer US [Place:] New York, NY [Year:] 2021 [Pages:] 1-41
Publisher: 
Springer US, New York, NY
Abstract: 
Adoption of carbon regulation mechanisms facilitates an evolution toward green and sustainable supply chains followed by an increased complexity. Through the development and usage of a multi-choice goal programming model solved by an improved algorithm, this article investigates sustainability strategies for carbon regulations mechanisms. We first propose a sustainable logistics model that considers assorted vehicle types and gas emissions involved with product transportation. We then construct a bi-objective model that minimizes total cost as the first objective function and follows environmental considerations in the second one. With our novel robust-heuristic optimization approach, we seek to support the decision-makers in comparison and selection of carbon emission policies in supply chains in complex settings with assorted vehicle types, demand and economic uncertainty. We deploy our model in a case-study to evaluate and analyse two carbon reduction policies, i.e., carbon-tax and cap-and-trade policies. The results demonstrate that our robust-heuristic methodology can efficiently deal with demand and economic uncertainty, especially in large-scale problems. Our findings suggest that governmental incentives for a cap-and-trade policy would be more effective for supply chains in lowering pollution by investing in cleaner technologies and adopting greener practices.
Subjects: 
Robust-heuristic optimization
Green supply chain
Sustainable supply chain
Sustainable logistics
Improved multi-choice goal programming
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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