Optimal exploitation for hybrid systems of renewable resources under partial observation

This work focuses on optimal controls for hybrid systems of renewable resources in random environments. We propose a new formulation to treat the optimal exploitation with harvesting and renewing. The random environments are modeled by a Markov chain, which is hidden and can be observed only in a Gaussian white noise. We use the Wonham filter to estimate the state of the Markov chain from the observable process. Then we formulate a harvesting-renewing model under partial observation. The Markov chain approximation method is used to find a numerical approximation of the value function and optimal policies. Our work takes into account natural aspects of the resource exploitation in practice: interacting resources, switching environment, renewing and partial observation. Numerical examples are provided to demonstrate the results and explore new phenomena arising from new features in the proposed model. (C) 2021 Elsevier Ltd. All rights reserved.