This paper considers the task allocation problem under the requirement that the assignments of some critical tasks must be maximized when the network capacity cannot accommodate all tasks due to the limited capacity for each unmanned aerial vehicle (UAV). To solve this problem, this paper proposes an extended performance impact algorithm with critical tasks (EPIAC) based on the traditional performance impact algorithm. A novel task list resizing phase is developed in EPIAC to deal with the constraint on the limited capacity of each UAV and maximize the assignments of critical tasks. Numerical simulations demonstrate the outstanding performance of EPIAC compared with other algorithms.

Dependence assessment, which is to assess the influence of the operator's failure of a task on the failure probability of subsequent tasks, is an important part in Human reliability analysis (HRA). The technique for human error rate prediction (THERP) has been widely applied to assess the dependence in HRA. However, due to the complexity of the real world, various kinds of uncertainty could occur in dependence assessment problem, and how to properly express and deal with uncertainty especially interval uncertainty remains a pressing issue. In this article, a novel method based on the interval evidential reasoning (IER) algorithm is proposed to assess dependence in HRA under interval uncertainty. First, dependence influential factors are identified and their functional relationship is determined. Then, judgments on these factors provided by the analysts are represented using interval belief distributions. Next, the interval evidential reasoning algorithm is employed to aggregate interval belief distributions of different factors according to their functional relationship while considering the credibility of the interval belief distribution. Finally, the conditional human error probability (CHEP) is calculated based on the fused interval belief distribution, where the upper and lower values are determined by assigning belief degree to the highest and lowest grade of the corresponding grade interval, respectively. Two numerical examples demonstrate that the proposed method not only properly deals with interval uncertainty using interval belief distribution and IER algorithm, but also provides a novel and effective way for dependence assessment in HRA.

Applying augmented reality (AR) glasses to overhead crane inspection can significantly improve the efficiency and accuracy of inspection operations. However, this technology also introduces new and complex risks stemming from the intricate interactions between people, machines, the environment, and management factors. Because traditional risk assessment methods implicitly assume factor independence, they struggle to capture this complexity. To overcome this deficiency, this study develops a Consequence-Prioritized Weighted Borda integrated with DEMATEL (CPWB-DEMATEL) hybrid framework specifically designed for risk assessment in AR-assisted inspection environments. First, an application-oriented risk identification framework is proposed by expanding SHELL model from a traditional four-dimension structure to include dedicated Personnel (P) and Management (M) dimensions, which leads to the systematic identification of 27 critical risk factors. Second, the weighted Borda severity ranking is combined with DEMATEL network centrality, ensuring that the comprehensive risk priority reflects both high inherent severity and high systemic influence. At the same time, this integration further enables a critical risk propagation path identification procedure that extracts multi-hop transmission chains from the DEMATEL influence matrix, revealing system-level risk propagation structures. Third, a case study conducted at a special equipment inspection agency validated the applicability of the framework. The results show that operator/rigger violations, signal personnel coordination errors, multi-task coordination failures, inadequate AR process supervision, and wire rope recognition errors are the most critical risk factors, with management deficiencies and communication-related factors also playing significant roles in risk propagation. Comparative analyses and sensitivity tests confirm the stability and robustness of the derived risk rankings across various parameter settings.