A Review on Optimization Techniques for Minimizing Makespan and Idle Time in Small-Scale Manufacturing Systems

Production planning and scheduling are critical functions in manufacturing industries for improving productivity, reducing operational cost, and enhancing resource utilization. In small-scale manufacturing systems, improper scheduling often leads to increased makespan, higher machine idle time, production delays, and reduced operational efficiency. This review paper presents a comprehensive study of optimization techniques used for minimizing makespan and idle time in production scheduling systems. The study reviews traditional scheduling methods along with advanced heuristic, metaheuristic, and machine learning-based optimization approaches such as Genetic Algorithm, Particle Swarm Optimization, Ant Colony Optimization, Simulated Annealing, Artificial Neural Networks, and Reinforcement Learning. Comparative analysis of various techniques is carried out based on scheduling efficiency, computational complexity, and industrial applicability. The paper also discusses recent advancements in Industry 4.0, AI-driven scheduling, IoT-enabled manufacturing systems, and hybrid optimization frameworks. Research gaps related to real-time scheduling, SME-focused applications, and intelligent adaptive systems are identified. The review concludes that hybrid machine learning and heuristic optimization approaches offer significant potential for improving manufacturing productivity, reducing idle time, and enabling smart production scheduling systems for future industrial applications.