Authors :
Farag M. Meragh Hossen; Ali F. Ali Fadiel; Magdi E.M. EL-Garosh
Volume/Issue :
Volume 9 - 2024, Issue 1 - January
Google Scholar :
http://tinyurl.com/yk9vbk45
Scribd :
http://tinyurl.com/mrxfnv35
DOI :
https://doi.org/10.5281/zenodo.10613238
Abstract :
In the ever-evolving landscape of
manufacturing, the need for efficient and adaptive
processes is paramount. This research delves into the
realm of manufacturing process optimization,
employing a novel approach that integrates
programming-driven simulation and control strategies.
The study begins with exploring the current state of
manufacturing optimization and identifying gaps and
challenges. A comprehensive methodology outlines the
simulation framework, programming techniques, and
control strategies implemented in the manufacturing
system under investigation.
The mathematical models used for simulation are
detailed, accompanied by a discussion of assumptions
and simplifications. The simulation results are then
presented, showcasing the proposed approach's
performance compared to baseline methods. The paper
further describes the implementation of control
strategies, providing insights into the coding structure,
design considerations, and the seamless integration with
the simulation framework. The results obtained from
the control measures are analyzed, offering a
comprehensive understanding of their impact on the
manufacturing process.
The discussion section interprets the findings,
highlighting their implications for the field of
manufacturing optimization. Comparative analyses
with existing studies underscore the uniqueness and
effectiveness of the proposed approach. Limitations and
challenges encountered during the research are
transparently discussed, paving the way for future
investigations. The conclusion succinctly summarizes
the key contributions of this research and outlines
recommendations for further exploration in this
interdisciplinary domain.
This paper advances the understanding of
manufacturing process optimization. It provides a
practical framework integrating programming-driven
simulation and control, offering a promising avenue for
enhancing efficiency and adaptability in contemporary
manufacturing environments.
Keywords :
Machine Learning, Manufacturing Optimization. Mathematical Modeling, Control Strategies, Manufacturing Simulation, Process Automation, Computational Optimization, Industry 4.0, Smart Manufacturing.
In the ever-evolving landscape of
manufacturing, the need for efficient and adaptive
processes is paramount. This research delves into the
realm of manufacturing process optimization,
employing a novel approach that integrates
programming-driven simulation and control strategies.
The study begins with exploring the current state of
manufacturing optimization and identifying gaps and
challenges. A comprehensive methodology outlines the
simulation framework, programming techniques, and
control strategies implemented in the manufacturing
system under investigation.
The mathematical models used for simulation are
detailed, accompanied by a discussion of assumptions
and simplifications. The simulation results are then
presented, showcasing the proposed approach's
performance compared to baseline methods. The paper
further describes the implementation of control
strategies, providing insights into the coding structure,
design considerations, and the seamless integration with
the simulation framework. The results obtained from
the control measures are analyzed, offering a
comprehensive understanding of their impact on the
manufacturing process.
The discussion section interprets the findings,
highlighting their implications for the field of
manufacturing optimization. Comparative analyses
with existing studies underscore the uniqueness and
effectiveness of the proposed approach. Limitations and
challenges encountered during the research are
transparently discussed, paving the way for future
investigations. The conclusion succinctly summarizes
the key contributions of this research and outlines
recommendations for further exploration in this
interdisciplinary domain.
This paper advances the understanding of
manufacturing process optimization. It provides a
practical framework integrating programming-driven
simulation and control, offering a promising avenue for
enhancing efficiency and adaptability in contemporary
manufacturing environments.
Keywords :
Machine Learning, Manufacturing Optimization. Mathematical Modeling, Control Strategies, Manufacturing Simulation, Process Automation, Computational Optimization, Industry 4.0, Smart Manufacturing.
