| dc.description.abstract | This research presents an advanced Genetic Algorithm (GA) designed for addressing the
complexities of the Flexible Job Shop Scheduling Problem (FJSSP), focusing on
dependent set-up times, non-preemption constraints, and machine eligibility. The
proposed GA integrates innovative strategies to navigate the intricate solution space,
handling temporal dependencies and non-preemption scenarios. It also incorporates
machine eligibility constraints to mirror real-world manufacturing scenarios.
Computational experiments validate the algorithm's efficiency, comparing it with state of-the-art algorithms. Results highlight its superior performance in terms of solution
quality, convergence speed, and robustness. This work contributes to optimizing
scheduling processes in diverse industrial applications, demonstrating the algorithm's
effectiveness in solving real-world instances of the FJSSP.. | en_US |
