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Modelling supply chain network for procurement of food grains in India

Author

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  • D. G. Mogale
  • Abhijeet Ghadge
  • Sri Krishna Kumar
  • Manoj Kumar Tiwari

Abstract

The procurement of food grains from farmers and their transportation to regional level has become decisive due to increasing food demand and post-harvest losses in developing countries. To overcome these challenges, this paper attempts to develop a robust data-driven supply chain model for the efficient procurement of food grains in India. Following the data collected from three leading wheat producing Indian regions, a mixed-integer linear programming model is formulated for minimising total supply chain network costs and determining number and location of procurement centres. The NK Hybrid Genetic Algorithm (NKHGA) is employed to cluster the villages, along with a novel density-based approach to optimise the supply chain network. Sensitivity analysis indicates that policymakers should focus on creating an adequate number of procurement centres in each surplus state, well before the start of the harvesting season. The study is expected to benefit food grain supply chain stakeholders such as farmers, procurement agencies, logistics providers and government bodies in making an informed decision.

Suggested Citation

  • D. G. Mogale & Abhijeet Ghadge & Sri Krishna Kumar & Manoj Kumar Tiwari, 2020. "Modelling supply chain network for procurement of food grains in India," International Journal of Production Research, Taylor & Francis Journals, vol. 58(21), pages 6493-6512, November.
    • Handle: RePEc:taf:tprsxx:v:58:y:2020:i:21:p:6493-6512
    DOI: 10.1080/00207543.2019.1682707
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    Cited by:

    1. Chang You & Shidong Zhang & Wenshu Liu & Luo Guo, 2024. "Localized Sustainable Development Goals Changes and Their Response to Ecosystem Services—A Case of Typical Southern Hilly Regions in China," Land, MDPI, vol. 13(7), pages 1-20, June.
    2. Chintapalli, Prashant, 2023. "Optimal multi-period crop procurement and distribution policy with minimum support prices," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
    3. Kumar, Shashank & Raut, Rakesh D. & Queiroz, Maciel M. & Narkhede, Balkrishna E., 2021. "Mapping the barriers of AI implementations in the public distribution system: The Indian experience," Technology in Society, Elsevier, vol. 67(C).

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