Humanitarian crises are escalating globally, with disaster-affected populations increasing by 23% annually. Yet traditional single-vehicle logistics systems fail catastrophically when infrastructure is compromised, creating life-threatening delays in critical supply delivery. While emerging truck-drone integrated systems show promise, existing research remains fragmented across disciplines, lacking systematic analysis of risk-aware optimization approaches and their real-world applicability. This study addresses two primary research objectives: (1) to develop a comprehensive taxonomy of mathematical models and solution methodologies for truck-drone humanitarian logistics, and (2) to quantify performance improvements and identify implementation barriers. A systematic review of 132 peer-reviewed papers from Scopus (2020–2026) was conducted using PRISMA methodology, complemented by bibliometric analysis of citation networks. The analysis reveals that hybrid solution approaches dominate the field (67 studies), with emergency response applications showing the strongest research focus (36 studies), though only about 40% report validation beyond purely simulated experiments. This review identifies three main contributions: a structured taxonomy of 15 distinct problem formulations and 8 solution categories, a quantitative benchmarking framework revealing performance trade-offs, and identification of critical research gaps in uncertainty modelling and stakeholder coordination. The results inform policy frameworks for regulatory approval while offering practitioners evidence-based counsel for decisions about technology adoption. This analysis lays the groundwork for more robust humanitarian logistics networks by advancing both theoretical knowledge and actual application of risk-aware truck-drone systems.
Augmented reality (AR) is increasingly used to support construction and industrial procedural work through field-integrated guidance. However, technical feasibility does not by itself ensure usable and attention-compatible interaction in AR systems. Existing evaluations commonly operate at the outcome level, but the connection between user feedback, interface redesign, and multimodal validation remains insufficiently traceable. This study developed and evaluated a feedback-driven redesign workflow for a construction scaffolding AR task assistance system. System A was tested through task execution and semi-structured interviews, and the resulting feedback was coded, prioritized, and translated into interface modifications in System B. A two-cohort counterbalanced within-subject evaluation compared the two versions using behavioral, subjective, and AOI-based eye-tracking evidence. Results showed that System B improved task performance, usability, and visual attention organization. The convergence of these evidence sources within the counterbalanced design supports the interpretation that the observed changes reflect genuine shifts in attention-interface interaction. The study contributes a traceable formative multimodal workflow for construction AR task assistance development, applicable to other industrial task-assistance contexts involving procedural guidance, hands-busy interaction, and attention-intensive work.
