Authors :
Emmanuel ADEDIRAN; Adewale SALAMI; Taiwo OGUNYINKA; Wilson SAKPERE
Volume/Issue :
Volume 8 - 2023, Issue 11 - November
Google Scholar :
https://tinyurl.com/7xtkx88k
Scribd :
https://tinyurl.com/289rmtjx
DOI :
https://doi.org/10.5281/zenodo.10212686
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 decision-making 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.
Keywords :
Computational, Linear Programming, Artificial Intelligence.
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 decision-making 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.
Keywords :
Computational, Linear Programming, Artificial Intelligence.
