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A fuzzy robust optimization model for dual objective forward and reverse logistics networks considering carbon
Yuepeng Shi1, Botang Li2, Maxim A Dulebenets3
1School of Energy and Intelligent Engineering, Henan University of Animal Husbandry and Economy, Zhengzhou, China.
Plos One
|March 11, 2025
Summary
This study presents a robust fuzzy programming model for designing low-carbon logistics networks amid uncertainty. The model optimizes facility locations and routes to minimize costs and emissions, ensuring reliable decision-making.
Area of Science:
- Operations Research
- Supply Chain Management
- Environmental Science
Background:
- Managing low-carbon integrated supply chain logistics is complex due to inherent unpredictability.
- Designing effective low-carbon logistics networks requires addressing uncertainty and policy considerations.
Purpose of the Study:
- To design a resilient low-carbon logistics network under uncertainty.
- To identify optimal facility locations and transportation routes.
- To minimize costs and reduce emissions through strategic planning.
Main Methods:
- Employed robust optimization and fuzzy programming techniques.
- Developed a multi-level forward/reverse integration logistics network model.
- Utilized extensive computational simulations for validation.
Main Results:
- The proposed robust fuzzy programming model demonstrates efficacy.
- Analytical results confirm the rationality and applicability of the optimization model.
- Decisions derived from the model have a 50% probability of being the most favorable outcomes.
Conclusions:
- The robust fuzzy programming approach provides a viable solution for designing low-carbon logistics networks.
- The model supports informed decision-making for cost minimization and emission reduction.
- The approach is applicable to complex, uncertain, and policy-influenced logistics systems.
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