<![CDATA[Linear programming (LP) plays a significant role as an analytical tool in environments where decision-making must be conducted under resource constraints. As an optimization method, LP models problems by defining decision variables, objective functions, and constraints to identify the most efficient solutions within given limits. In managerial and operational contexts, LP assists in allocating scarce resources effectively to maximize outcomes such as profit or operational efficiency, enabling stakeholders to make objective, data-driven choices. Recent applications of LP span from business planning to decision support systems that optimize promotional strategies, demonstrating how structured mathematical models can guide complex decision processes. Moreover, LP is integrated into educational contexts to strengthen analytical reasoning, helping learners link theoretical formulations with practical economic choices. This article synthesizes findings from recent literature to illustrate how LP fosters systematic decision-making, enhances clarity in trade-offs between alternatives, and supports rational choices in constrained environments. The reviewed studies confirm that LP not only serves as a computational optimization technique but also reinforces structured thinking essential for decision quality when resources are limited. Hence, LP remains a valuable method for both practitioners and scholars in decision sciences.]]>
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