Artificial intelligence

Abstract: . The objective of this study is to develop a scheduling algorithm for executing a set of non-periodic tasks performed by a support or maintenance department of an enterprise. The proposed approach ranks the importance of tasks based on a combination of input factors, such as the requester’s status, problem criticality, resource availability, task complexity, and urgency. A two-level fuzzy inference framework, based on the Mamdani algorithm, is proposed to address prioritization challenges in user support systems. The framework operates sequentially in two stages: in the first stage, interdependent input criteria are processed pairwise to obtain intermediate results, and in the second stage, these results are aggregated to determine the final task priority. To describe the input and output variables in the implementation of the inference framework, a fuzzy methodology utilizing triangular membership functions was chosen, ensuring a balance between precision and smoothness in defuzzification. The proposed framework was tested on a resource allocation problem within technical support departments, where time and resource constraints must be considered while simultaneously processing a set of requests. The developed approach can be applied to optimize resource allocation volumes required for executing a set of tasks characterized by importance, criticality, complexity, and urgency metrics.

Keywords: fuzzy inference, Mamdani algorithm, two-stage model, triangle membership functions, task prioritization, task executing fuzzy time.

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