Sustainable Harvesting Policies Under Long-Run Average Criteria: Near Optimality

Sustainable Harvesting Policies Under Long-Run Average Criteria: Near Optimality Appl Math Optim https://doi.org/10.1007/s00245-018-9504-y Sustainable Harvesting Policies Under Long-Run Average Criteria: Near Optimality 1 2 Dang H. Nguyen · George Yin © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract This paper develops near-optimal sustainable harvesting strategies for the predator in a predator-prey system. The objective function is of long-run average per unit time type in the path-wise sense. To date, ecological systems under environmental noise are usually modeled as stochastic differential equations driven by a Brownian motion. Recognizing that the formulation using a Brownian motion is only an ide- alization, in this paper, it is assumed that the environment is subject to disturbances characterized by a jump process with rapid jump rates. Under broad conditions, it is shown that the systems under consideration can be approximated by a controlled diffusion system. Based on the limit diffusion system, control policies of the original systems are constructed. Such an approach enables us to develop sustainable har- vesting policies leading to near optimality. To treat the underlying problems, one of the main difficulties is due to the long-run average objective function. This in turn, requires the handling of a number of issues related to ergodicity. New approaches are developed http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Mathematics and Optimization Springer Journals

Sustainable Harvesting Policies Under Long-Run Average Criteria: Near Optimality

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Publisher
Springer US
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Mathematics; Calculus of Variations and Optimal Control; Optimization; Systems Theory, Control; Theoretical, Mathematical and Computational Physics; Mathematical Methods in Physics; Numerical and Computational Physics, Simulation
ISSN
0095-4616
eISSN
1432-0606
D.O.I.
10.1007/s00245-018-9504-y
Publisher site
See Article on Publisher Site

Abstract

Appl Math Optim https://doi.org/10.1007/s00245-018-9504-y Sustainable Harvesting Policies Under Long-Run Average Criteria: Near Optimality 1 2 Dang H. Nguyen · George Yin © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract This paper develops near-optimal sustainable harvesting strategies for the predator in a predator-prey system. The objective function is of long-run average per unit time type in the path-wise sense. To date, ecological systems under environmental noise are usually modeled as stochastic differential equations driven by a Brownian motion. Recognizing that the formulation using a Brownian motion is only an ide- alization, in this paper, it is assumed that the environment is subject to disturbances characterized by a jump process with rapid jump rates. Under broad conditions, it is shown that the systems under consideration can be approximated by a controlled diffusion system. Based on the limit diffusion system, control policies of the original systems are constructed. Such an approach enables us to develop sustainable har- vesting policies leading to near optimality. To treat the underlying problems, one of the main difficulties is due to the long-run average objective function. This in turn, requires the handling of a number of issues related to ergodicity. New approaches are developed

Journal

Applied Mathematics and Optimization – Springer Journals

Published: May 28, 2018

References

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