Hybrid Metaheuristic Optimization for Multi-Objective Scheduling in Smart Manufacturing Systems

Intelligent manufacturing involves scheduling methods that maximize efficiency, use of energy, and cost. This paper introduces a hybrid metaheuristic framework which combines the exploration improvement capability of Genetic Algorithms (GA) and the exploitation power of Particle Swarm Optimization (PSO). The suggested merge GA-PSO model develops Job Shop Scheduling Problem (JSSP) as a multiobjective optimization task with the function of minimum makespan, energy consumed, and operational cost according to the induction function. The framework is validated by means of both standard JSSP benchmarks as well as Industry 4.0 inspired datasets. Experimental results show the significant performance improvements, i.e., 18.6 % makespan reduction, 12.4 % energy consumption reduction and 15.2 % operational cost reduction based on standalone GA, PSO and baseline methods. In addition, the Pareto-front diversity and convergence speed of GA--PSO are better, and hence, more effective trade-offs can be achieved among conflicting objectives. These outcomes have established GA-PSO as a strong and efficient scheduling approach for realtime, energy awareness and cost-optimized operations in the Industry 4.0 environment to pave the way for sustainable and intelligent manufacturing systems.