Enhancing Operational Efficiency: Harnessing Metaheuristic Algorithms for Scheduling in Cellular Manufacturing

In Cellular Manufacturing (CM) scheduling, a set of components must be processed in cells by a set of machines. One of the key decisions in cell production is timing of components in each cell. The goal is to find the sequence of performing pieces in each group and the sequence of performing groups of pieces in cells on a set of machines; in such a way that the desired criterion is optimized in the schedule. This proposed work explores various meta-heuristic approaches to solve the CM scheduling problem and compare it with the existing state-of-theart Genetic Algorithm. The linear problem of scheduling for 6 jobs along with their completion time in 3 cells using meta-heuristic algorithms is implemented. The algorithms are evaluated based on fitness score computed in terms of completion time and load balancing score. In this minimization problem, the different proposed algorithms have outperformed the Genetic algorithm in various aspects. Particle swarm optimization has found to have the minimum fitness score