A Metaheuristic Approach to Job Shop Scheduling Using Cat Swarm Optimization Algorithm

Cat Swarm Optimization (CSO) is a metaheuristic algorithm that is inspired by cat behavior. While solving the optimization problem, the seeking and tracing modes of CSO balance the exploration and exploitation. This study developed the CSO algorithm to address the Job Shop Scheduling Problem (JSSP). JSSP is the assignment of tasks to available machines. The objective of scheduling is to reduce the overall completion duration, considering the precedence constraint. Efficient scheduling is crucial in manufacturing and production planning. In this research paper, the CSO algorithm framework is integrated with an active schedule strategy. By transforming candidate solutions into active schedules in each iteration, the algorithm investigates only significant areas of the search space and avoids infeasible or poor solutions. The proposed metaheuristic is evaluated on Lawrence instances. Results demonstrate that the CSO with an active schedule strategy improves solution quality and convergence rate relative to the basic PSO methods. This approach gives an effective way to address JSSP in the manufacturing sector.