Information-Centric Optimization of Bio-Based Supply Chains Using Hybrid C-IFS and Type-2 Fuzzy MAIRCA
Strategic decision-making in Sustainable Bio-Based Supply Chains (SBSCs) is increasingly hindered by semantic ambiguity, expert inconsistency, and the inability of conventional models to handle uncertainty. These challenges compromise the alignment of AI integration with sustainability goals, especially in complex, multi-criteria environments. This study develops an AI-based decision support framework that addresses uncertainty and complexity in evaluating SBSCs, leveraging hybrid fuzzy logic methodologies.
The shift toward AI-enabled SBSCs introduces multifaceted sustainability trade-offs and challenges rooted in linguistic ambiguity, subjective judgments, and expert inconsistency. Existing methods often lack the semantic resilience required for strategic supply chain planning under uncertainty.
This study proposes an integrated hybrid approach combining Circular Intuitionistic Fuzzy Sets (C-IFS) with a Type-2 Fuzzy MAIRCA method. The framework operates in two stages: (1) C-IFS aggregation and entropy-based filtering to derive robust criteria weights and manage low-consensus indicators; and (2) application of Type-2 Fuzzy MAIRCA to assess and prioritize AI-integrated SBSC alternatives against idealized performance targets using interval-valued fuzzy distances.
The proposed model introduces a novel fusion of advanced fuzzy logic techniques to enable transparent, interpretable, and information-centric evaluation of bio-based supply chain configurations in uncertain environments.
Among the evaluated SBSC configurations, the fully integrated AI-driven model (A2) achieved the highest sustainability score (0.872) with the lowest performance gap (Ψ = 0.486), confirming its strategic alignment. Scenario testing under semantic shifts upheld the model’s robustness and highlighted digital infrastructure as a key sensitivity factor.
Practitioners can adopt the proposed framework to improve the strategic alignment of AI implementations in SBSCs, enabling more resilient, data-driven decisions across operational and regulatory dimensions.
Researchers are encouraged to explore extensions of this hybrid approach to other high-uncertainty domains and to examine the integration of additional soft computing methods for enhanced decision fidelity.
By supporting sustainable and technologically adaptive supply chains, this framework contributes to greener production ecosystems and informed policymaking in bioeconomy sectors.
Future studies may incorporate dynamic feedback systems, real-time AI learning, or bibliometric modeling to enrich the methodological scope and cross-sector applicability of the framework.
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