Decision-Making and Operational Research Methods: A Theoretical-Cognitive Approach Personal Experience

This article examines the relationship between decision-making and operations research methods, focusing on their evolution and application in complex environments. The main objective is to analyze how tools such as linear programming, simulation, and queue theory optimize resources and improve efficiency through the incorporation of advanced technologies like artificial intelligence and predictive analytics. These methods have been essential in solving problems across various sectors such as manufacturing, transportation, and services. Interviews with 50 participants, including academics and students, were used to assess the effectiveness of various operational methods in decision-making. The primary criterion was efficiency and improvement in the results obtained, while secondary results included the perception of flexibility and practical applicability of the methods. Simulation emerged as the most efficient approach, with a 22% improvement over other methods. Simulation methods proved to be the most effective, improving results by 22% compared to other approaches like integer and nonlinear programming. Participants highly valued practical and flexible methods, such as simulation, due to their ability to adapt to different contexts and situations. Additionally, the ease of implementation and time savings in decision-making were key factors in their preference. The study confirms that simulation is a key tool in decision-making in complex operational environments, providing superior results in efficiency. This suggests that, for future research, further exploration of the integration of practical and flexible methods like simulation, along with the exploration of their potential in combination with emerging technologies, is needed to optimize real-time decision-making processes. It is also recommended to evaluate their implementation in other sectors beyond those analyzed in this study, such as healthcare and public management.