Metaheuristic Approaches for Flexible Lot-sizing and Scheduling with Remanufacturing and Sequence-dependent Setup Time

This study addresses a critical research gap in production planning by proposing an integrated model for flexible lot-sizing and scheduling that simultaneously considers remanufacturing, sequence-dependent setup times, and energy efficiencyfactors often studied in isolation in previous research.The model reflects the complexity of modern manufacturing systems, where products may return for remanufacturing and machines require specific setup times depending on operation sequences.The novelty of this work lies in its holistic approach, combining these elements within a flexible job-shop environment, which better captures the dynamics and constraints of real-world production settings.To efficiently solve this NP-hard problem, the study develops and applies three metaheuristic algorithms: Genetic Algorithm (GA), Whale Optimization Algorithm (WOA), and Particle Swarm Optimization (PSO).These algorithms are evaluated on a range of problem sizes to assess their scalability and performance.The key contribution is twofold: first, in the formulation of a realistic and comprehensive mathematical model, and second, in demonstrating the effectiveness of metaheuristic methods for solving complex large-scale problems.Results indicate that while all three algorithms produce feasible and high-quality solutions, the GA consistently achieves superior outcomes, making it a robust and efficient approach for optimizing lot-sizing and scheduling decisions in sustainable and flexible manufacturing environments.