Artificial Intelligence; A computational and Linear Programming approach

Abstract

Artificial intelligence (AI) has become a potent tool for resolving complicated issues in a 
variety of fields. This study investigates how computational and linear programming 
approaches, in particular, may be used to solve practical problems. The computational side of 
AI includes creating models and algorithms that replicate human reasoning and decisionmaking processes. A branch of mathematical optimization known as "linear programming" 
tries to maximize or minimize a linear objective function while taking into account a number 
of linear constraints. Combining these strategies will enable us to maximize efficiency, increase 
decision-making, and better allocate resources across a variety of industries, including banking, 
logistics, healthcare, and manufacturing. The theoretical underpinnings of computational and 
linear programming approaches are examined in this study, and case studies showing how they 
have been successfully used in practical scenarios. The results highlight the effectiveness of 
AI-driven computational and linear programming approaches in tackling complex problems, 
ultimately leading to improved outcomes and increased productivity. As AI continues to 
advance, further research and innovation in these areas hold great promise for solving even 
more intricate challenges in the future.