Optimizing queuing disciplines with discrete event simulation: A theoretical and empirical review
Keywords:
operations research, cost-benefit analysis, programming modelsAbstract
The analysis of service systems that implement queuing with priority discipline has gained importance in sectors where operational efficiency is critical. These models are fundamental to optimize the use of resources, reducing waiting times and improving system response to fluctuating demands. The integration of prioritized queuing theories and discrete event simulation facilitates the evaluation of operational strategies under varying conditions, allowing managers to adapt resources more effectively. Recent research has incorporated elements of fuzzy logic to address uncertainty in arrival and service rates, providing a more flexible and robust framework for queue modeling. Studies suggest that well-managed systems not only improve performance indicators, but also support data-driven strategic decision making. With the advancement of artificial intelligence and machine learning technologies, it is anticipated that prioritized queuing models will offer even more predictive and adaptive capabilities, marking a significant development in operations management and IT systems. This field remains prolific for research, with potential to revolutionize practices in industries that rely on efficient queuing and service management.
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