A Constraint-Aware Cuckoo Search Metaheuristic for Resource-Limited Production Scheduling and Makespan Minimization

Production scheduling in industrial settings requires the simultaneous coordination of tasks, machines, operators, and precedence relations under limited resource availability. This study proposes a Constraint-Aware Cuckoo Search Algorithm (CACSA) for resource-constrained production scheduling and makespan minimization. The modification combines CSA exploration with a constraint-aware and operator-aware repair layer that converts continuous Levy-flight perturbations into feasible discrete schedules by selecting admissible machine–operator pairs, respecting precedence relations, and applying local repair-guided refinement within the same computational budget. The experimental section combines instance-level schedule documentation with comparative statistical evaluation. The documented instance set includes Scenarios I-VI and two 35-task variants, while the repeated comparison uses 30 independent runs per method and an equal computational budget for CACSA, baseline CSA, Genetic Algorithm, Particle Swarm Optimization, Differential Evolution, Simulated Annealing, and Ant Colony Optimization. The evaluation reports best, mean, standard deviation, worst makespan, runtime, feasibility rate, lower-bound gaps, and parameter sensitivity. The results show that CACSA preserves feasibility in all tested scenarios and generally improves baseline CSA performance, with the clearest gains observed in the larger 35-task instances. The study therefore positions the proposed CACSA as a practical and reproducible scheduling approach, while distinguishing feasibility, robustness, and comparative dominance as separate empirical claims.