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Showing 3 results for Stochastic Dynamic Programming
Mohammadi Limaei, Lohmander, Obersteiner,
Volume 2, Issue 1 (4-2010)
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.
Samimi, Aghaie, Shahriari,
Volume 3, Issue 2 (9-2012)
Abstract
We deal
with the relationship termination problem in the context of individual-level customer
relationship management (CRM) and use a Markov decision process to determine
the most appropriate occasion for termination of the relationship with a
seemingly unprofitable customer. As a particular case, the
beta-geometric/beta-binomial model is considered as the basis to define
customer behavior and it is explained how to compute customer lifetime value
when one needs to take account of the firm’s choice as to whether to continue
or terminate relationship with unprofitable customers. By numerical examples provided
by simulation, it is shown how a stochastic dynamic programming approach can be
adopted in order to obtain a more precise estimation of the customer lifetime
value as a key criterion for resource allocation in CRM.
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.
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