An optimized multi-attribute decision-making approach to construction supply chain management by using complex...

Area of Science:

• Decision Sciences
• Fuzzy Set Theory
• Operations Research

Background:

• Supplier selection is crucial for organizational success but is complicated by inherent uncertainties.
• Existing multi-criteria decision analysis and fuzzy logic methods struggle with complex uncertainties.
• A novel approach is needed to effectively manage vagueness and information-based uncertainties in supplier selection.

Purpose of the Study:

• To develop a generalized fuzzy set structure, the complex picture fuzzy soft set (cpFSS), for addressing information-based uncertainties in supplier selection.
• To introduce amplitude and phase terms within cpFSS for managing fuzzy membership and periodicity.
• To enable decision-makers to incorporate neutral opinions into the evaluation process.

Main Methods:

• Investigation of cpFSS notions and set-theoretic operations with illustrative examples.
• Development of a multi-attribute decision-making (MADM) algorithm using cpFSS matrix-based aggregations (core matrix, decision value matrices, score).
• Implementation of the proposed algorithm in a real-world construction project supplier selection case.

Main Results:

• The cpFSS structure effectively handles information-based uncertainties and periodicity.
• The proposed MADM algorithm, utilizing cpFSS, provides consistent supplier rankings.
• Sensitivity analysis using Pythagorean means confirms the robustness of the results.

Conclusions:

• The complex picture fuzzy soft set (cpFSS) provides a more flexible and reliable framework compared to existing fuzzy set structures for decision-making under uncertainty.
• The developed MADM algorithm is effective for real-world supplier selection problems, ensuring consistent and reliable outcomes.
• The cpFSS approach enhances decision-making by accommodating neutral opinions and managing complex uncertainties.