A two-stage stochastic programming approach to design the fish supply chain network considering export revenues and carbon emission: a real case study

Official URL: https://dx.doi.org/10.1007/s12351-025-00994-2

In recent decades, rapid economic growth has had a detrimental impact on the environment, posing a significant global threat to food security. On the other hand, food supply chains are facing challenges in coping with the growing demand. This paper introduces a two-stage stochastic mixed-integer linear programming approach to design a fish supply chain network that satisfies both domestic demand for fish and foreign demand for fish fillets. To address environmental concerns, a carbon tax is implemented, levied per ton of greenhouse gas emissions emitted during transportation. To account for the uncertain nature of demand, the model is formulated as a two-stage stochastic program by considering demand as an uncertain parameter. To validate the applicability of the presented model, a case study is conducted on the trout fish supply chain in Iran. The sample average approximation (SAA) method is used to solve the proposed model by approximating solutions to the stochastic model with an infinite number of scenarios. Computational results are obtained from different sample sizes, and various fundamental parameter values are investigated to provide valuable managerial insights. As a result, the proposed method has yielded high-quality solutions based on the achieved statistical upper and lower bounds. The results also demonstrate the positive impact of higher carbon taxes on the environment, with a lower loss in profitability. Furthermore, the Genetic Algorithm (GA) has been developed to accelerate the solving time of the presented model. The results obtained from GA demonstrate a higher quality of solutions when compared to SAA methods.