Modelling supply chain network for procurement of food grains in India

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.

Keywords:

• food supply chain
• modelling and optimisation
• food grains
• clustering
• genetic algorithm
• procurement

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the research project: Procurement, Collection and Storage of Food Grains (CAG), No. – IIT/SRIC/IEM/CAG/2013-14/206 under the mega project Sustainable food Security through: Technological interventions for production, processing and logistics (SAL) No. – IIT/SRIC/AGFE/SAL/2013-14/159, sponsored by MHRD, Department of Higher Education, New Delhi, India.