Enhanced ELECTRE III method with interval-valued hesitant fuzzy linguistic sets for multi-criteria group decision-making in smart supply networks

Abstract

This study presents a robust decision-making framework for evaluating strategic artificial intelligence (AI) initiatives within DHL's smart supply network. The objective is to prioritize four AI alternatives-autonomous warehouse routing, predictive delivery optimization, AI-driven demand forecasting, and intelligent inventory rebalancing-based on eight strategic criteria, including cybersecurity, adaptability, and infrastructure readiness. A cross-functional panel of experts provided linguistic assessments, modeled using Interval-Valued Hesitant Fuzzy Linguistic Term Sets (IVHFLTS) to capture hesitation and uncertainty. These inputs were aggregated and processed by an extended ELECTRE III method incorporating fuzzy thresholds for indifference, preference, and veto. Sensitivity analysis confirmed the stability of the final ranking under ±10% threshold variation, while consensus evaluation revealed expert divergence, which was mitigated by dynamic reweighting. Predictive delivery optimization and intelligent inventory rebalancing emerged as the top-ranked initiatives, aligning with DHL's strategic goals of customer responsiveness and operational resilience. The methodology demonstrates high robustness, interpretability, and practical relevance for AI-driven logistics planning.