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Integrated site selection framework for origin-based cold storage using GIS-MCDM and improved Harris Hawks optimization
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  • Published: 02 March 2026

Integrated site selection framework for origin-based cold storage using GIS-MCDM and improved Harris Hawks optimization

  • Yujie Li1,2,3,
  • Fengyu Li3,
  • Xinting Yang1,2,
  • Xianyong Meng3 &
  • …
  • Jiawei Han1,2 

Scientific Reports , Article number:  (2026) Cite this article

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Subjects

  • Engineering
  • Environmental sciences
  • Mathematics and computing

Abstract

A well-planned layout for origin-based cold storage is crucial for minimizing post-harvest losses, reducing costs, improving logistics efficiency, and mitigating environmental impacts. To address gaps in existing research on multi-technology coordination and trade-offs among siting objectives, this paper proposes an integrated multi-method framework that combines Geographic Information Systems (GIS), multi-criteria decision making (MCDM), and an improved Harris Hawks Optimization (IHHO) algorithm for facility siting optimization. We develop an evaluation system that incorporates logistics infrastructure, natural conditions, and agricultural development and use a GIS-MCDM model with spatial constraints to delineate highly suitable areas. K-medoids spatial clustering and an economies-of-scale cost model are then used, with IHHO determining the optimal number, locations, and capacities of facilities. A case study in Helan County, China, indicated that highly suitable zones are concentrated in the south, accounting for approximately 1.25% of the study area; nine candidate regions were identified, and six optimal sites were selected. Scenario analysis revealed that higher fixed construction costs favor larger facilities, while growing demand supports centralized, high-capacity cold stores rather than dispersed, smaller ones. Overall, the proposed framework provides a systematic tool for scientific planning and suitability assessment of on-farm cold-chain infrastructure, with the potential to enhance logistics efficiency, reduce postharvest losses, and promote sustainable agricultural development.

Data availability

Requests for access to the data supporting this study may be submitted to the corresponding author, Dr. Jiawei Han.

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Acknowledgements

This work was supported by the Scientific and Technological Innovation Capacity Building Project of Beijing Academy of Agricultural and Forestry Sciences (KJCX20251003), Key Research and Development Program of Ningxia Hui Autonomous Region, China (2024BEG02031), National Key Research and Development Program of China (2023YFD2001302-03), and National Engineering Laboratory for Agri-product Quality Traceability, Beijing Academy of Agricultural and Forestry Sciences, China (PT2025-24). We are thankful to Professor Jiawei Han from the Information Technology Research Center of Beijing Academy of Agriculture and Forestry Sciences for his valuable guidance.

Funding

This research was funded by the Scientific and Technological Innovation Capacity Building Project of Beijing Academy of Agricultural and Forestry Sciences (KJCX20251003), Key Research and Development Program of Ningxia Hui Autonomous Region, China (2024BEG02031), National Key Research and Development Program of China (2023YFD2001302-03), and National Engineering Laboratory for Agri-product Quality Traceability, Beijing Academy of Agricultural and Forestry Sciences, China (PT2025-24).

Author information

Authors and Affiliations

  1. Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China

    Yujie Li, Xinting Yang & Jiawei Han

  2. National Engineering Laboratory for Agri-product Quality Traceability, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China

    Yujie Li, Xinting Yang & Jiawei Han

  3. School of Information Science and Engineering, Shandong Agricultural University, Tai’an, 271018, China

    Yujie Li, Fengyu Li & Xianyong Meng

Authors
  1. Yujie Li
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  2. Fengyu Li
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Contributions

Y.L. Writing–original draft, Conceptualization, Investigation, Methodology, Software, Visualization, Validation. Y.L., F.L., and X.Y. Data curation. Y.L. and F.L. Formal analysis. F.L. and X.Y. Project administration. X.M. and J.H. Supervision. Y.L., X.M., and J.H. Writing–review and editing. X.Y. and J.H. Funding acquisition. All authors reviewed the manuscript.

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Correspondence to Jiawei Han.

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Li, Y., Li, F., Yang, X. et al. Integrated site selection framework for origin-based cold storage using GIS-MCDM and improved Harris Hawks optimization. Sci Rep (2026). https://doi.org/10.1038/s41598-026-40766-2

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  • Received: 15 October 2025

  • Accepted: 16 February 2026

  • Published: 02 March 2026

  • DOI: https://doi.org/10.1038/s41598-026-40766-2

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Keywords

  • Origin-based cold storage
  • Geographic information system
  • Multi-criteria decision-making
  • Optimization model
  • Site selection decision
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