Designing robust green sustainable supply chain network by bi-objective optimization method

Abstract

Designing a green supply chain is becoming popular in the context of sustainable development. To address this academic concern, this paper designs a multi-product, multi-echelon green supply chain network (GSCN) from economic and environmental aspects. During the modeling process, the main challenge is to access the accurate probability distributions of uncertain parameters from limited historical data. To overcome this difficulty, this paper develops a distributionally robust design framework for bi-objective GSCN, where the distribution information of uncertain parameter is partially available and characterized by ambiguity sets. For the tractability, this paper discusses robust counterpart reformulation under Wasserstein-distance-based ambiguity sets. Finally, the obtained mixed integer programming model is resolved via commercial optimization software. To validate the proposed optimization framework, we design a meat supply chain network for a Chinese realistic food enterprise. The computational results demonstrate that the proposed distributionally robust model can provide reliable solutions compared with stochastic optimization method.

Keywords:

• Supply chain network design
• sustainability
• bi-objective optimization
• ambiguity set
• robust counterpart reformulation

Acknowledgments

Shanshan Gao and Yankui Liu contributed equally to this work. The authors are especially thankful to Editor-in-Chief, and anonymous reviewers for their valuable comments, which help authors to improve the paper a lot.

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Funding

This work is supported by the Natural Science Foundation of Hebei Province [No. A2023201020], the Operations Research and Management Innovation Team of Hebei University [No. IT2023C02], and the National Natural Science Foundation of China [No. 61773150].