reducing environmental pollutions in closed-loop supply chain optimization through fuzzy complex integer programming

The escalating volumes of greenhouse gases and pollutants, along with growing concerns about environmental issues, have compelled organizations and management academics to design networks that prioritize optimizing environmental factors and reducing pollutants within an economic framework. supply chain management stands out as a critical business issue that impacts all organizational activities by enhancing production, improving quality, cutting costs, and delivering superior customer service. this study aims to enhance total profit within the supply chain while concurrently reducing environmental pollution. the research methodology employed here is descriptive-analytical. a dual-objective mathematical model is proposed for the design of a closed-loop supply chain. this model is built upon criteria such as product count, recycled product count, and item count. a solution approach is suggested using dual-objective fuzzy programming. subsequently, the model is tested using available data through gams optimization software. the increase in the volume of the first objective function shows a linear relationship with the variation coefficient, whereas changes in demand exhibit a nonlinear relationship with the second objective function. specifically, an increase in demand leads to a significant rise in environmental pollution. it is evident that fluctuations in demand can substantially impact the total costs of the supply chain. the application of this developed model can help mitigate the adverse effects on the environment while promoting a sustainable image of the supply chain through cost reduction and profit enhancement.