Improving Linear Programming via Artificial Intelligence Techniques: An Experimental Study
DOI:
https://doi.org/10.31272/jae.i143.1197Keywords:
Linear Programming, Genetic Algorithm Artificial Intelligence, Machine Learning, Deep LearningAbstract
Operations research is one of the main fields in management sciences and industrial engineering. One of the most important of these fields is Linear Programming, which includes essential techniques, including: (improving production processes through applying techniques and tools such as network analysis, principal factor analysis, and regression analysis, in addition to designing... Models for improving the supply of primary resources, planning and controlling supply chains, and improving data analysis techniques and artificial intelligence in making strategic decisions, such as Big Data, Machine Learning, and Deep Learning. Linear programming techniques also contributed to improving quality and risk management by reducing risks associated with operations, such as total quality management, experiment design, risk analysis, and designing flexible systems to help innovate and develop products. Techniques for linking linear programming with artificial intelligence are of great importance, as they have helped develop systems and technologies that are characterised by the ability to learn, think, and make decisions like humans, for example (using artificial intelligence to improve planning and analysis processes in linear programming problems). In our study, one of the artificial intelligence techniques in linear programming was highlighted: the Genetic Algorithm (GA). The research aims to find the optimal values for the variables of the objective function in light of the restrictions imposed on a specific linear programming problem and, through optimisation, the value of the function. The objective achieved the most excellent profits compared to the objective function calculated within the Simplex Method. The fitness function was also calculated based on the data in the problem, and the results were found using the MATLAB R2019a program.
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