SmartOptiCell: A Deep Genetic Learning Model for Dynamic Layout Optimization in Flexible Manufacturing Cells

Abstract

Flexible Manufacturing Cells (FMCs) form the backbone of flexible automation and productivity in modern industries.
Still, dynamically optimizing the layout of these cells is subject to a myriad of production and system constraints,
which is a continually evolving problem. In this work, we present a solution to this problem using modern AI called
SmartOptiCell, a deep genetic learning model that combines deep learning with genetic algorithms. Built as an
extension of deep-leaning predictive models, the proposed model can predict optimal configurations for cell layouts
in an FMC by leveraging historical data and performing genetic algorithm-based operations on them. SmartOptiCell
can reduce the idle time of machines and material handling costs and enable greater responsiveness to changes in
demand. Through extensive simulations and benchmark comparisons, SmartOptiCell has been shown to outperform
competing models in dynamic environments, proving its efficacy as a decision-support model for future intelligent
automated manufacturing systems.