Real-time path optimization in green logistics using the honey bee algorithm

This study develops a real-time multi-objective path optimization framework for green logistics using the Honey Bee Algorithm (HBA) to address the limitations of conventional routing approaches under dynamic traffic and environmental conditions. The proposed framework simultaneously optimizes fuel consumption, carbon emissions, and delivery time, thereby integrating operational efficiency with environmental sustainability. A simulation-based experimental design is employed to compare HBA with three established metaheuristic algorithms—Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO)—under controlled logistics scenarios. The results demonstrate that HBA consistently outperforms the benchmark algorithms, reducing fuel consumption by approximately 20% and carbon emissions by approximately 25%, while achieving on-time delivery performance of up to 95%. These results indicate that the adaptive exploration–exploitation mechanism of HBA enables more effective route selection under dynamic and uncertain transportation conditions. The findings further demonstrate that incorporating environmental objectives directly into routing decisions can simultaneously improve operational efficiency and sustainability performance without imposing impractical computational demands. The proposed framework therefore provides a scalable decision-support approach for logistics firms, particularly in urban and last-mile delivery environments where traffic conditions and delivery requirements change dynamically. Overall, the study contributes empirical evidence on the effectiveness of bio-inspired optimization for sustainability-oriented logistics and demonstrates the potential of real-time multi-objective routing to support simultaneous improvements in transportation efficiency and environmental performance.

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