A two-stage stochastic optimization model for port cold storage capacity allocation considering pelagic fishery yield uncertainties

In the distant-water fishing industry, the catching of fish is extremely unpredictable, which leads to great uncertainties regarding timely amounts of fish in port cold storage. Therefore, research dedicated to capacity optimization of port cold storage is conducted under the scenario of distant-water fishery yield uncertainties. A two-stage stochastic programming model is established to optimize the cold storage capacity allocation considering pelagic fishery yield uncertainties. The first-stage decisions of the proposed model are related to the determination of port cold storage capacity, and the second-stage decisions are related to the daily amount of total catch in cold storage. First-stage decisions are made using the expected value of the penalty function, the objective of which is to minimize the cost due to the unsatisfied constraint condition. A case study in northern China is then presented to investigate the specific influence of uncertainties on the port cold storage capacity allocation.