| dc.description.abstract | This thesis addresses the Flexible Job Shop Scheduling Problem (FJSP) with multiple time
constraints using both exact and metaheuristic methods. The study develops a mathematical model
incorporating constraints such as machine breakdown times, maintenance schedules, and job
priority orders, solved using IBM's CPLEX for exact optimization. Additionally, a Genetic
Algorithm (GA) approach is proposed to handle larger, more complex instances, enhancing
solution quality and computational efficiency. Comparative analysis demonstrates the strengths
and limitations of each method, suggesting promising directions for hybrid optimization
techniques. The results highlight the potential for improved scheduling performance in dynamic
and constrained manufacturing environments. | en_US |
