A Multi-objective Cross Entropy-based algorithm for sustainable global food supply chain with risk considerations: A case study

Abstract

This paper proposes a sustainable global food supply chain model with risk considerations. Perishable foods begin to deteriorate after were produced. Therefore, without proper maintenance and transportation, the inventories may deteriorate quickly before be used which leads to unavoidable costs. The proposed model has four levels; suppliers, producers, warehouses and demand centers. The sustainable supply chain has three objectives, the first and second one is to reduce economic costs and environmental impacts, respectively and the third one is to maximize the level of employment in the chain. Also, because of nature of the supply chain, several risk factors are investigated in the model including: time delay risk, exchange rate fluctuations, raw material quality and production quality. For solving large instances, a Multi-Objective Cross Entropy (CE) algorithm is introduced. The proposed CE method is efficient in terms of its run time and low difference with the GAMS software. The MID and SM criteria which show the dispersion of the solutions are lower by CE method than the Epsilon constraint method. Also, a robust optimization approach is introduced to overcome the uncertainty. Also, a validation method is performed to evaluate the effectiveness of the robust approach. Then Sensitivity analysis is done to show capability of the model. Finally, the problem is applied for a food company in Iran and the city of Mashhad. The results show the establishment of a new factory in Qazvin lead to increase 2.8% of total costs, whereas decreasing 27.4% of CO2 emissions and increasing 44.2% of social benefits is resulted.