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The operation of RFID systems often involves a situation in which multiple readers physically located near one another may interfere with one another's operation. Such reader collision must be minimized to avoid the faulty or miss reads. This paper, therefore, aims to use a successful swarm intelligence technique called artificial bee colony (ABC) algorithm to minimize both the reader-toreader interference and total system transaction time in RFID reader networks. As the RFID network scheduling model formulated in this work is a discrete problem, a binary version of artificial bee colony (BABC) algorithm is proposed in this study. Unlike the original ABC algorithm, the proposed BABC represents a food source as a discrete binary variable and applies discrete operators to change the foraging trajectories of the employed bees, onlookers and scouts in the probability that a coordinate will take on a zero or one value. Numerical results for four test cases with different scales, which ranging from 10 to 200 readers, have been presented to demonstrate the performance of the proposed methodology.

There are inextricable links between the occurrence of accidents and the lack of employees' safety literacy. Researching on safety literacy evaluation is of great significance to find the deficiencies of practitioners in safety literacy. Based on social network analysis, this article makes a centrality analysis to analyze the indicators of safety literacy model, determines 16 core indicators of safety literacy model, and constructs a safety literacy index system. This article uses the fuzzy comprehensive evaluation method to evaluate the safety literacy model and the level of safety literacy of the employees. The actual case study shows that the safety literacy evaluation system can effectively determine the safety literacy level of employees and explain the impact of safety literacy on personal unsafe behavior, corporate organizational behavior, and enterprise safety production.

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