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Showing 3 results for Lohmander
Mohammadi Limaei, Lohmander, Obersteiner, Volume 2, Issue 1 (vol 2. No 1 2010)
Abstract
The optimal harvesting policy is calculated as a function of the entering stock, the price state, the harvesting cost, and the rate of interest in the capital market. In order to determine the optimal harvest schedule, the growth function and stumpage price process are estimated for the Swedish mixed species forests. The stumpage price is assumed to follow a stochastic Markov process. A stochastic dynamic programming technique and traditional deterministic methods are used to obtain the optimal decisions. The expected present value of all future profits is maximized. The results of adaptive optimization are compared with results obtained by the traditional deterministic approach. The results show a significant increase in the expected economic values via optimal adaptive decisions.
Dr. S Mohammadi Limaei, Dr. Peter Lohmander, Volume 8, Issue 1 (4-2017)
Abstract
We present a stochastic dynamic programming approach with Markov chains for optimal control of the forest sector. The forest is managed via continuous cover forestry and the complete system is sustainable. Forest industry production, logistic solutions and harvest levels are optimized based on the sequentially revealed states of the markets. Adaptive full system optimization is necessary for consistent results. The stochastic dynamic programming problem of the complete forest industry sector is solved. The raw material stock levels and the product prices are state variables. In each state and at each stage, a quadratic programming profit maximization problem is solved, as a subproblem within the STDP algorithm.
Dr. Peter Lohmander, Volume 8, Issue 2 (5-2017)
Abstract
We present a stochastic optimal control approach to wildlife management. The objective value is
the present value of hunting and meat, reduced by the present value of the costs of plant damages
and traffic accidents caused by the wildlife population. First, general optimal control functions and
value functions are derived. Then, numerically specified optimal control functions and value
functions of relevance to moose management in Sweden are calculated and presented.
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