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Solution of supply chain management problems using rough t spherical fuzzy set lower and upper approximation spaces
Xiaojie Zhao1, Yanping Zhou2, Lu Sun3
1College of Economics and Management, Shandong University of Science and Technology, Qingdao, 266590, Shandong, China. zhaoxj1415@163.com.
Scientific Reports
|May 11, 2026
Summary
This study introduces a new multi-attribute decision-making (MADM) model using rough t-spherical fuzzy Dombi operations for supply chain management. The novel approach enhances decision accuracy for selecting optimal supply chain systems.
Area of Science:
- Operations Research
- Management Science
- Fuzzy Set Theory
Background:
- Supply chain management (SCM) is crucial for organizational efficiency, customer satisfaction, and competitiveness.
- Assessing the optimal SCM system involves complex multi-attribute decision-making (MADM).
- Existing MADM models face limitations in handling imprecise and complex information.
Purpose of the Study:
- To develop an advanced MADM model for SCM system selection.
- To integrate rough t-spherical fuzzy sets (Rt-SFS) with Dombi operations for robust data aggregation.
- To introduce novel rough t-spherical fuzzy Dombi weighted averaging (Rt-SFDWA) and geometric (Rt-SFDWG) operators.
Main Methods:
- Utilized the rough t-spherical fuzzy set (Rt-SFS) framework for its generality and approximation spaces.
- Incorporated Dombi t-norm (DTNM) and t-conorm (DTCNM) operations for flexible fuzzy information aggregation.
- Developed a new MADM algorithm based on the proposed Rt-SFDWA and Rt-SFDWG operators.
Main Results:
- The proposed MADM algorithm effectively handles complex, multi-attribute decision problems in SCM.
- Sensitivity analysis confirmed the validity and robustness of the developed theory.
- The new model demonstrated superior accuracy compared to existing methods in a real-world SCM scenario.
Conclusions:
- The novel Rt-SFS Dombi-based MADM approach provides a precise and accurate method for SCM system selection.
- This framework offers a versatile tool for decision-making with incomplete and fuzzy information.
- The study validates the effectiveness of integrating advanced fuzzy set theories with MADM for practical SCM challenges.
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