期刊名称：Modern Supply Chain Research and Applications
印刷版ISSN：2631-3871
出版年度：2020
卷号：2
期号：3
页码：161-177
DOI：10.1108/MSCRA-04-2020-0007
语种：English
出版社：Emerald publishing Limited
摘要：Purpose Due 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/approach A 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. Findings The 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/implications In 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/value The 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.
