Two-stage mean-risk stochastic optimization model for port cold storage capacity under pelagic fishery yield uncertainty

Abstract

The problem of the optimal capacity of cold storage for pelagic fisheries under uncertain harvesting/production is studied. We establish a two-stage mean-risk stochastic optimization model, by considering the uncertainty of pelagic fishery yield and the risk measure of the cold storage cost loss. Applying a Benders-type scenario decomposition method, a modified cutting decomposition algorithm is proposed to solve the two-stage mean-risk stochastic optimization model, yielding the optimal capacity and maximal expected return of cold storage simultaneously. Further, the effects of the refrigeration cost, storage fee, weight of the conditional value-at-risk, and the confidence level on the expected profit are analyzed. We compare the modified cutting decomposition algorithm with a multi-cutting decomposition algorithm, to validate the proposed algorithm based on the computational time and the number of iterations.