Ergonomic Logistics Optimization: Multi-Objective Ant Colony and Fuzzy Logic Approach for Fatigue Management - Berlianty, Intan; Arifin, Miftahol; Insani, Mohamad Kamil (International Journal of Industrial Engineering: Theory, Applications and Practice, 2026)
The rapid growth of last-mile logistics has intensified concerns regarding worker fatigue, particularly in tropical operating environments characterized by high temperature, humidity, noise, and time pressure. While most logistics optimization studies focus primarily on minimizing distance, time, and operational costs, limited attention has been given to integrating ergonomic and physiological factors into decision-making models. This gap highlights the need for optimization approaches that balance operational efficiency with worker health and safety. This study proposes an integrated ergonomic logistics optimization framework that combines Multi-Objective Ant Colony Optimization with a Takagi–Sugeno Fuzzy Inference System to jointly address routing efficiency and fatigue management. The proposed model incorporates environmental exposure indicators and physiological workload measures to estimate fatigue risk and embed it within multi-objective decision processes. The framework operates through fatigue prediction, optimization of delivery routes under ergonomic constraints, and adaptive evaluation of rest scheduling policies. The results indicate that the integrated approach produces more balanced solutions compared to conventional distance-based optimization strategies, improving system performance while mitigating excessive fatigue accumulation. The findings also reveal the limitations of static regulatory rest standards when applied to dynamic and high-stress logistics contexts. Theoretically, this study extends multi-objective metaheuristic optimization by embedding human-centered performance variables into logistics modeling. Practically, it provides a decision-support mechanism for fatigue-aware route planning and adaptive work-rest management. Overall, the research advances the development of data-driven, ergonomically informed logistics systems that promote sustainable operational performance and worker well-being.