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Compromising allocation for optimising agri-food supply chain distribution network: a fuzzy stochastic programming approach

Author

Listed:
  • Srikant Gupta

    (Jaipuria Institute of Management)

  • Sachin Chaudhary

    (Autonomous State Medical College)

  • Rajesh Kr Singh

    (Management Development Institute)

  • Jose Arturo Garza-Reyes

    (The University of Derby)

  • Vikas Kumar

    (Birmingham City University)

Abstract

The management of Agri-food supply chains is a complex task, given the unique product characteristics, perishability, uncertain demand, and specific storage requirements. This research introduces an innovative approach to optimizing product allocation among producers, brokers, wholesalers, and retailers, focusing on minimizing transportation costs and network delivery time through multi-objective programming. To address uncertainties, supply and demand constraints are modelled using a gamma distribution, and the maximum likelihood estimation method determines their parameters with specified probabilities. The study conducts a case analysis to showcase the model’s practical effectiveness, and a numerical comparison with alternative approaches is included. The primary goal of this study is to enhance the efficiency of agri-food supply chain management practices, providing valuable insights for practitioners in the field, with a focus on cost reduction and improved delivery time.

Suggested Citation

  • Srikant Gupta & Sachin Chaudhary & Rajesh Kr Singh & Jose Arturo Garza-Reyes & Vikas Kumar, 2024. "Compromising allocation for optimising agri-food supply chain distribution network: a fuzzy stochastic programming approach," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(6), pages 2019-2041, June.
    • Handle: RePEc:spr:ijsaem:v:15:y:2024:i:6:d:10.1007_s13198-023-02234-2
    DOI: 10.1007/s13198-023-02234-2
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    References listed on IDEAS

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