Genetic Algorithm Approach for Smart Industrial Multi-Objective Production Planning

The fourth industrial revolution has ushered in a transformative era for the industrial sector, marked by the integration of advanced technologies that increase production efficiency and quality. Among these innovations is Smart Scheduling, which aims to optimize production processes while minimizing costs and meeting manufacturing requirements. This article explores the development and application of a Hybrid Genetic Algorithm, optimized by Tabu Search, to meet the complex challenge of multi-objective industrial production planning. The proposed framework introduces a Genetic Algorithm designed for intelligent, multi-objective industrial production planning, demonstrating its effectiveness in generating high-quality planning. It significantly reduces production times while maintaining quality and business needs, as validated in a real-world case study. The algorithm improves on traditional planning methods by dynamically adapting to manufacturing priorities. It also describes potential future adaptations to a wider range of industrial contexts and sectors. This research highlights the fundamental role of genetic algorithms in advancing smart manufacturing, offering a scalable and adaptable solution to the challenges of Industry 4.0.