2018 年 24 巻 1 号 p. 33-36
Fish is one of the most important primary sources of animal protein for human consumption globally. Fish harvesting strategy in artificial fish ponds is important to fish-farmers who practice commercial aquaculture under uncertainties of market prices. Robust optimization was developed to solve uncertain optimization problems by considering the uncertain data to be existing in an uncertainty set. In this study, we consider that growth models for market price of fish with volatility are given and then analyze harvesting policy for fish in the framework of discrete robust optimization. Demonstrative optimization is performed using a robust counterpart optimization technique, which involves numerical solution of nonlinear algebraic equations systems, with hypothetical model parameters. With discrete harvesting stages in the time domain, the robust optimal harvesting policy under presence of volatility is prescribed as a partial harvesting strategy.