Advancing Supplier Selection: Evaluating Fuzzy MCDM vs. Quantum-Inspired Optimization in High-Dimensional, Uncertain Decision Spaces

Supplier selection remains a highly complex multi-criteria decision-making (MCDM) challenge, particularly in environments characterized by uncertainty, incomplete information, and dynamic market fluctuations. Traditional fuzzy MCDM methods (e.g., Fuzzy AHP, Fuzzy TOPSIS, Fuzzy ITARA) have been widely adopted for their ability to handle linguistic imprecision and expert-driven evaluations, making them well-suited for procurement decision-making. However, the advent of quantum-inspired optimization (QIO) algorithms, which exploit principles of quantum superposition, entanglement, and probabilistic heuristics, presents an alternative paradigm that claims superior efficiency in solving high-dimensional combinatorial optimization problems.

This raises several critical academic and methodological challenges that remain underexplored:

1. Computational Complexity vs. Decision Interpretability

While QIOs claim exponential speedup in solving large-scale supplier selection problems, their lack of explainability and interpretability could hinder practical adoption in procurement. To what extent can QIOs outperform traditional fuzzy MCDM methods in real-world procurement applications, considering the trade-offs between computational complexity, solution quality, and decision transparency?

2. Uncertainty Representation in Supplier Selection Models

Fuzzy logic excels at modeling qualitative uncertainty and linguistic vagueness, whereas quantum-inspired approaches rely on probabilistic distributions and non-classical optimization heuristics. Can QIOs effectively model vague, preference-driven supplier evaluation criteria, or do they require hybridization with fuzzy-based uncertainty modeling to enhance robustness?

3. Empirical Benchmarking and Industrial Feasibility

Despite theoretical claims, empirical studies comparing QIOs with fuzzy MCDM methods in procurement remain scarce. What are the empirical performance benchmarks in terms of solution convergence, computational efficiency, and procurement cost optimization, particularly in real-world datasets? Given the limitations of Noisy Intermediate-Scale Quantum (NISQ) computing, how practical is QIO for procurement optimization, and what are the short-term and long-term adoption barriers?

4. Hybrid Fuzzy-Quantum Decision Frameworks

Could a hybrid fuzzy-quantum model bridge the interpretability-efficiency gap by leveraging the strengths of fuzzy logic in linguistic modeling while incorporating the computational advantages of quantum-inspired heuristics? What hybrid methodologies could be explored to enhance procurement decision robustness in dynamic, multi-stakeholder supply chain environments?

Given these open research questions, I invite scholars and practitioners specializing in quantum computing, fuzzy logic, procurement science, uncertainty modeling, and AI-driven supply chain optimization to share insights, empirical findings, and theoretical advancements on:

• The fundamental trade-offs between fuzzy logic-based MCDM and QIO in procurement decision-making.
• Comparative studies or case studies benchmarking these methodologies in real-world supplier selection problems.
• The feasibility of integrating QIOs into procurement decision systems given current technological constraints.
• Hybrid approaches that combine fuzzy MCDM with quantum-inspired heuristics for superior decision-making under uncertainty.