Modelling supply chain network for procurement of food grains in India. Issue 21 (1st November 2020)

• Record Type: Journal Article
• Authors: Mogale, D. G.; Ghadge, Abhijeet; Kumar, Sri Krishna; Tiwari, Manoj Kumar
• Is Part Of: International journal of production research. Volume 58:Issue 21(2020)
• Page Start: 6493
• Page End: 6512
• Publication Date: 2020-11-01
• Subjects: food supply chain -- modelling and optimisation -- food grains -- clustering -- genetic algorithm -- procurement; Factory management -- Periodicals; 658.57
• DOI: 10.1080/00207543.2019.1682707
• Languages: English
• ISSNs: 0020-7543

Abstract

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.