A Hybrid Genetic Algorithm for Minimizing Weighted Tardiness in a Hybrid Flow Shop: A Case Study in the Electronics Industry

The smart meter manufacturing industry is characterized by high product diversity and large production volumes, which create a complex and dynamic environment for production planning and scheduling. Managing this complexity requires robust decision-support approaches capable of balancing efficiency and flexibility. This study proposes a Hybrid Genetic Algorithm (HGA) to minimize total weighted tardiness in a four-stage Hybrid Flow Shop scheduling problem inspired by a real case in the electronics manufacturing sector. The production system includes multiple parallel machines with sequence-dependent setup times, limited buffer capacities, unrelated parallel machines, synchronization constraint and machine eligibility constraints. The proposed HGA integrates a constructive heuristic based on the Earliest Due Date rule with setup-aware local optimization to generate high-quality initial solutions. Parameter tuning was performed using the Taguchi experimental design, and the significance of algorithmic factors was assessed through Analysis of Variance. Experimental results using real industrial data demonstrate that the HGA achieves superior performance compared to heuristic and manual scheduling methods, reducing total weighted tardiness by up to 40.3\%. Managerially, the approach enhances contractual compliance, supply chain resilience, and decision-making efficiency. The findings confirm the scalability and robustness of the proposed algorithm for large, constraint-intensive scheduling environments and highlight future research directions involving hybrid metaheuristics.