Abstract
PurposeDue to unceasing declination in environment, sustainable agro-food supply chains have become a topic of concern to business, government organizations and customers. The purpose of this study is to examine a problem associated with sustainable network design in context of Indian agro-food grain supply chain.Design/methodology/approachA mixed integer nonlinear programming (MINLP) model is suggested to apprehend the major complications related with two-echelon food grain supply chain along with sustainability aspects (carbon emissions). Genetic algorithm (GA) and quantum-based genetic algorithm (Q-GA), two meta-heuristic algorithms and LINGO 18 (traditional approach) are employed to establish the vehicle allocation and selection of orders set.FindingsThe model minimizes the total transportation cost and carbon emission tax in gathering food grains from farmers to the hubs and later to the selected demand points (warehouses). The simulated data are adopted to test and validate the suggested model. The computational experiments concede that the performance of LINGO is superior than meta-heuristic algorithms (GA and Q-GA) in terms of solution obtained, but there is trade-off with respect to computational time.Research limitations/implicationsIn literature, inadequate study has been perceived on defining environmental sustainable issues connected with agro-food supply chain from farmer to final distribution centers. A MINLP model has been formulated as practical scenario for central part of India that captures all the major complexities to make the system more efficient. This study is regulated to agro-food Indian industries.Originality/valueThe suggested network design problem is an innovative approach to design distribution systems from farmers to the hubs and later to the selected warehouses. This study considerably assists the organizations to design their distribution network more efficiently.
Highlights
India is an agricultural nation with majority of its population reliant on agriculture in a direct or indirect manner (Gouda et al, 2018)
A mathematical model is framed with an objective of minimizing the transportation and carbon tax cost and satisfying different constraints linked with food grain supply chain
The code has been run on LINGO and MATLAB software on I7 processor supply chains and 8 GB RAM, 1 TB ROM in windows 10 platform
Summary
India is an agricultural nation with majority of its population reliant on agriculture in a direct or indirect manner (Gouda et al, 2018). (1) A mathematical model is suggested to minimize the costs related with transportation and carbon emissions via centralized planning for the movement of food grains from farms to the purchase hubs and subsequently to the required warehouses in the country. Sustainable agro-supply chain it can be observed as identifying the surplus food producing region, developing proper purchase and logistic systems, ensuring good returns to the stakeholders, ensuring good product to the customers and reducing the carbon footprint. The farmers travel to the nearest market hub results in high transportation costs, carbon emissions and reduced profitability for the farmers To counter this situation, a centralized assembly of the food products from the farmers’ location to the hub is provided. A mathematical model is framed with an objective of minimizing the transportation and carbon tax cost and satisfying different constraints linked with food grain supply chain. LINGO is a proficient for resolving different forms of programming methods such as linear and nonlinear programming, mixed integer programming, quadratic programming, etc. (Prajapati et al, 2020)
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