To address the complexities of managing networks of unmanned aerial vehicles (UAVs) and Just-in-Time problem solving, this study introduces a cutting-edge multi-objective location-routing optimization model. This model integrates time window constraints, concurrent pick-up and delivery demands, and rechargeable battery functionality, significantly enhancing the efficiency of UAV operations. It reduces battery consumption and transportation costs while optimizing delivery times and reducing operational risks. The model improves the refinement of delivery schedules by accounting for uncertain traffic scenarios, thereby increasing its accuracy and reliability in dynamic environments. Additionally, a Bayesian belief networks approach for risk assessment introduces a new layer to operational risk management. The model’s performance and its trade-offs are demonstrated through advanced data visualizations such as 3D Pareto fronts, pair plots, and network graphs, with validation via the NSGA-II approach confirming its reliability and practical applicability. This research represents a major leap forward in drone routing strategies, focusing on efficiency, adaptability, and risk management in UAV logistics and provides a comprehensive framework that bridges the gap between theoretical exploration and practical application.

As developing countries increasingly rely on information and communication technologies (ICTs) for structural transformation, understanding the nexus between digital infrastructure, governance, and technological progress becomes critical for effective telecommunications policy. This study provides a novel empirical investigation into the simultaneous interrelationships among digital economy development, institutional quality (encompassing regulatory and governance frameworks), and innovation efficiency. Utilizing a comprehensive panel dataset of 81 developing countries over the period 2002–2023, this research overcomes endogeneity issues by employing a Simultaneous Equations Model (SEM) estimated via the GMM-2SLS estimator. This is complemented by Stochastic Frontier Analysis (SFA) to extract innovation efficiency and a Panel Vector Autoregression (PVAR) model to establish causality structures. The empirical results reveal a robust, positive bidirectional Granger causality among all three dimensions. Specifically, advancements in telecommunications and digital infrastructure directly enhance both institutional transparency and innovation efficiency, while robust regulatory quality concurrently drives digital investments and R&D performance. The findings offer critical insights for policymakers and telecom regulators in developing countries, suggesting that targeted investments in telecommunication networks and proactive, high-quality regulatory frameworks are mutually reinforcing and act as essential prerequisites for maximizing innovation capacity in the digital era.