MULTI-OBJECTIVE OPTIMIZATION OF NON-IDENTICAL PARALLEL MACHINE SCHEDULING FOR MINIMIZING MAKESPAN, CARBON EMISSIONS, AND TARDINESS - Ramadhan, Fairuzzaky; Rifai, Achmad Pratama (ASEAN Journal of Systems Engineering, 2026)
Production scheduling in modern manufacturing must simultaneously address operational efficiency, environmental sustainability, and customer satisfaction. As industries face increasing pressure to reduce carbon emissions while maintaining competitiveness, traditional single-objective scheduling approaches prove insufficient. Non-identical parallel machine scheduling problem (NIPMSP), where machines possess heterogeneous processing capabilities and environmental impacts, represents a prevalent configuration in manufacturing facilities. However, existing research has not simultaneously optimized makespan, carbon emissions, and tardiness—three critical objectives reflecting productivity, sustainability, and service reliability.In this study, we developed a comprehensive mathematical model with 18 constraints and proposed Multi-Objective Adaptive Large Neighborhood Search (MOALNS) incorporating five destroy operators, four repair operators, and adaptive weight mechanisms. Performance evaluation against NSGA-II across eight problem instances using five metrics (Hypervolume, Inverted Generational Distance, Diversification Metric, Spacing Metric, CPU Time) with rigorous statistical testing (Wilcoxon signed-rank test, Cohen's d) revealed that MOALNS significantly outperforms NSGA-II in solution diversity (p < 0.05 in 50% of instances, d = 6.31) and spacing uniformity (75% win rate, d = 0.70), with comparable Pareto front coverage. This superiority comes at computational cost (192% slower), acceptable for offline planning but not real-time applications. This research provides evidence-based algorithm selection guidelines for sustainable manufacturing, demonstrating that environmental objectives can be integrated without sacrificing operational efficiency or delivery reliability.