供应链
业务
互联网
营销
供应链管理
产业组织
物联网
过程管理
商业
计算机科学
广告
万维网
作者
Pan Li-hong,Xialian Li,Miyuan Shan
标识
DOI:10.3390/jtaer20020137
摘要
Designing a sustainable supply chain network for perishable products is challenging due to their short shelf life and sensitivity to environmental conditions. These factors necessitate strict quality control and efficient logistics. The emergence of Internet of Things (IoT) technology has significantly improved supply chain operations by enabling real-time monitoring of environmental conditions. This helps maintain product quality and ensures timely deliveries. Additionally, using mixed fleets—comprising both electric and conventional vehicles—can reduce carbon emissions without compromising operational reliability. While previous studies have explored the application of IoT to enhance delivery efficiency and the use of mixed fleets to address environmental concerns, few have examined both technologies within a unified modeling framework. This study proposes a sustainable multi-period supply chain network for perishable products that integrates IoT technology and mixed fleets into an optimization framework. We develop a multi-objective location-inventory-routing model. The first objective minimizes total costs, including production, facility operation, inventory, transportation, carbon emissions, IoT deployment, and energy use. The second objective aims to maximize service levels, which are measured by product quality and on-time delivery. The model is solved using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). A case study based on real-world data demonstrates the model’s effectiveness. Sensitivity analysis indicates that balancing the emphasis on quality and delivery reliability leads to improved cost and service performance. Furthermore, while total costs steadily increase with higher demand, service levels remain stable, showcasing the model’s robustness. These results provide practical guidance for managing sustainable supply chains for perishable products.
科研通智能强力驱动
Strongly Powered by AbleSci AI