AI-Driven Optimization of Logistics and Open-Pit Mining Fleet Operations: Integrative Architectures, Predictive Analytics, and Emerging Connectivity Paradigms

Abstract

This article synthesizes theoretical foundations, methodological approaches, and practical implications for AI-driven optimization of logistics systems and open-pit mining fleet operations. Building strictly upon the provided literature, it constructs a comprehensive conceptual framework that integrates ensemble machine learning for cost optimisation in commercial logistics, AI-driven predictive analytics for pharmaceutical procurement and supply centralization, high-bandwidth communications enabling real-time routing and delivery (6G), remote sensing methods for urban and non-urban point extraction, last-mile delivery strategies including drone integration, and advanced discrete-event and evolutionary optimisation methods for truck-shovel allocation and dispatch in mining. The structured analysis articulates problem formulations, proposes method-of-methods combinations (ensemble learning + discrete-event simulation + multi-objective evolutionary algorithms), and explicates evaluation criteria focused on economic, operational, and sustainability outcomes. Results are described qualitatively through descriptive analysis that translates algorithmic outputs into operational decisions, and implications are interpreted across multiple levels: strategic procurement and centralization effects on drug availability and health outcomes, tactical fleet scheduling and energy efficiency, and operational routing and last-mile feasibility under emerging connectivity infrastructures. Limitations, counter-arguments, and future research priorities are examined in depth, emphasising the importance of validation, explainability, and system-of-systems thinking when deploying AI in safety- and cost-critical domains. The article concludes with an integrated set of recommendations for researchers and practitioners seeking to design, validate, and scale AI-enabled logistics and mining fleet systems that balance efficiency, robustness, and ethical transparency.