Bakan, Hacer ÖzBakan, Hacer OzYilmaz, FikriyeWeber, Gerhard-WilhelmMathematics2024-07-052024-07-05201741619-697X1619-698810.1007/s10287-017-0286-52-s2.0-85023765369https://doi.org/10.1007/s10287-017-0286-5https://hdl.handle.net/20.500.14411/2883Yılmaz, Fikriye/0000-0003-0002-9201; OZ BAKAN, HACER/0000-0001-8090-5552; Weber, Gerhard-Wilhelm/0000-0003-0849-7771In this paper, we obtain the discrete optimality system of an optimal harvesting problem. While maximizing a combination of the total expected utility of the consumption and of the terminal size of a population, as a dynamic constraint, we assume that the density of the population is modeled by a stochastic quasi-linear heat equation. Finite-difference and symplectic partitioned Runge-Kutta (SPRK) schemes are used for space and time discretizations, respectively. It is the first time that a SPRK scheme is employed for the optimal control of stochastic partial differential equations. Monte-Carlo simulation is applied to handle expectation appearing in the cost functional. We present our results together with a numerical example. The paper ends with a conclusion and an outlook to future studies, on further research questions and applications.eninfo:eu-repo/semantics/closedAccessStochastic optimal controlOptimal harvestingStochastic partial differential equationsSymplectic partitioned Runge-Kutta schemesArticleQ3144519533WOS:000424442700004