A Bi-level Optimization Modeling for Perishable Food Supply Chain: The Case of A Warm-water Farmed Fish Supply Chain in Iran

Sustainable supply management of food resources and to distribute high-quality food products throughout a profitable supply chain are the most important issues for societies management. In recent years, uncontrolled fishing in the Caspian Sea, the Persian Gulf, and the Oman Sea has resulted in drastic decreases in the reserves of these water resources. Expansion of aquaculture farms not only has contributed to the development of a sustainable source of food for the country but also has been highly effective in the preservation of species that are endangered for whatever reason. Today, seafood and related products have been known for job creation and earning foreign exchange. In this research, we develop a novel Stackelberg modeling and optimization framework for perishable food supply chain that addresses warm-water farmed fish supply chain planning by formulating a bi-level optimization model. This paper proposes an algorithm called bi-level PSO benefits from Particle Swarm Optimization (PSO) co-evolutionary algorithm that maximizes the fish farms and stalls profits. To validate the proposed algorithm, Iran warm-water fish supply chain market data was collected; implementation of the model on the Iran fish markets shows its applicability to real life logistics networks.