Designing a sustainable, resilient, and responsive wheat supply chain under mixed uncertainty: A multi-objective approach

Abstract

The stability and long-term viability of agri-food supply chains are at risk due to factors such as rapid population growth and global disruptions like the COVID-19 pandemic. This study develops a multi-objective robust fuzzy stochastic programming model to integrate sustainability, resilience, and responsiveness in designing agri-food supply chains operating under uncertain conditions. The sustainability-related objectives of the proposed model aim to reduce the overall cost and negative environmental and social effects. The resilience-related objectives in this model focus on decreasing the node criticality, complexity, and customer dissatisfaction. Additionally, minimizing the total delivery time serves as a measure of responsiveness. In this study, the Torabi and Hassini (TH) interactive fuzzy programming method and the Best-Worst Method (BWM) are applied as a solution approach to handle the multi-objective model. The proposed multi-objective model is applied to a Canadian wheat supply chain, considering its distinct features like blending, wheat quality, weather, and intermodal transport. Moreover, comprehensive sensitivity analyses are carried out to examine the effects of changing the model's parameters on the objective functions. Finally, the performance of the presented model is evaluated by conducting a comparative analysis. The results demonstrate the model's ability to handle hybrid uncertainty and provide tradeoffs between various objectives, which can benefit decision-makers.