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

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