Compromising allocation for optimising agri-food supply chain distribution network: a fuzzy stochastic programming approach
Journal article
Authors | Gupta, S., Chaudhary, S., Kumar Singh, R, Garza-Reyes, J. and Kumar, V. |
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Abstract | Managing Agri-food supply chains is complex, 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. Supply and demand constraints are modelled using a gamma distribution to address uncertainties, 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, focusing on cost reduction and improved delivery time. |
Keywords | Multi-Objective Optimization; Supply Chain Network; Stochastic Programming; Gamma Distribution |
Year | 2024 |
Journal | International Journal of System Assurance Engineering and Management |
Journal citation | pp. 1-23 |
Publisher | Springer |
ISSN | 0976-4348 |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s13198-023-02234-2 |
Web address (URL) | https://link.springer.com/article/10.1007/s13198-023-02234-2 |
Accepted author manuscript | License All rights reserved File Access Level Controlled |
Output status | Published |
Publication dates | |
Online | 16 Jan 2024 |
Publication process dates | |
Deposited | 05 Feb 2024 |
https://repository.derby.ac.uk/item/q45y3/compromising-allocation-for-optimising-agri-food-supply-chain-distribution-network-a-fuzzy-stochastic-programming-approach
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