Designing a Sustainable Supply Chain Network for Perishable Products Integrating Internet of Things and Mixed Fleets

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.

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Publication Details

Journal
Journal of theoretical and applied electronic commerce research
Published
2025-06-06
DOI
https://doi.org/10.3390/jtaer20020137
Citations
3
Primary Topic
Advanced Manufacturing and Logistics Optimization
Type
article
Field-Weighted Citation Impact
3.73
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article

Designing a Sustainable Supply Chain Network for Perishable Products Integrating Internet of Things and Mixed Fleets

Miyuan Shan, Xialian Li, Pan Li-hong
3 citations
Journal of theoretical and applied electronic commerce research
Advanced Manufacturing and Logistics Optimization
3.73
article

Designing a Sustainable Supply Chain Network for Perishable Products Integrating Internet of Things and Mixed Fleets

Miyuan Shan, Xialian Li, Pan Li-hong
article en
3 citations

Abstract

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.

Journal of theoretical and applied electronic commerce researchVol. 20(2)
Hunan University (CN), Hunan Institute of Engineering (CN)
Openalex Percentile: Top 7%
Advanced Manufacturing and Logistics Optimization
3.73
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