Simplex and interior point methods foe solving budgetary allocation linear programing problem in industry revolution 4.0

TL;DR

This paper explores the application of simplex and interior point methods for solving complex, real-world linear programming problems in Industry 4.0, focusing on budgetary allocation to optimize profit.

Contribution

It develops and compares simplex and affine interior point methods for LP problems, providing insights into their applicability in Industry 4.0 contexts.

Findings

LP models effectively optimize budget allocation in Industry 4.0

Affine interior point method shows promising performance in real-world LP problems

Comparison results guide future research on LP solution techniques

Abstract

Increasing the complexity of solving budgetary allocation (NP-hardness problem) has led a wide range of methods to minimize the costs. Metaheuristics and Linear Programming (LP) are the most optimization in this fields. Therefore, this study provides insights and a deep understanding of the applicability of LP models in industry and how to formulate SM and affine IPM for solving real world linear problems. Moreover, it will present a better way to deal with decision making problems through the development and comparison of the SM and affine IPM to solve LP optimization problem to maximize profit. Finally, to other researchers particularly of similar interests who are undertaking further investigation on this topic, this study can be vital as a secondary source of information and guidance.