In this paper we discuss the generator investment problem for a newly proposed energy-only market structure comprising both spot and forward sub-markets as an alternative long-term resource adequacy solution. The inve...
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(纸本)9781424419050
In this paper we discuss the generator investment problem for a newly proposed energy-only market structure comprising both spot and forward sub-markets as an alternative long-term resource adequacy solution. The investment problem is modeled as stochastic dynamic programming problem for a profit maximizing generator over a long time horizon. The long-term growth and short-term deviation of demand are represented as stochastic processes. The spot market is modeled as bilevel noncooperative game and the forward market is formulated based on mean-variance criteria and market equilibrium arguments. The interrelated dynamics of different markets and its effect on investment decision and profitability of market participants are analyzed and comparisons with other market structures such as spot only energy markets are investigated as well.
The coefficients of Taylor's [Taylor, J.B., 1993. Discretion versus policy rules in practice. Camegie Rochester Conference Series on Public Policy 39, 195-214] monetary policy rule can be seen as portfolio weights...
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The coefficients of Taylor's [Taylor, J.B., 1993. Discretion versus policy rules in practice. Camegie Rochester Conference Series on Public Policy 39, 195-214] monetary policy rule can be seen as portfolio weights. Their optimal values are derived by adapting Merton's [Merton, R.C., 1971. Optimum consumption and portfolio rules in a continuous-time model. Journal of Economic Theory 3, 373-413) asset allocation model. (c) 2007 Elsevier B.V. All rights reserved.
Nowadays in business environment, marketing competitiveness is as demanding as ever. To survive under keen competitions, industries must keep acquiring customers and make them loyal while maximizing profit from their ...
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Nowadays in business environment, marketing competitiveness is as demanding as ever. To survive under keen competitions, industries must keep acquiring customers and make them loyal while maximizing profit from their service subscription or product purchasing. Intensive research works have been done in answering when and what kind of promotions should be used under limited marketing communication resources to maintain a perpetual generation of revenue. In this paper, we investigate the advantages in consecutive promotion based on the framework of the model proposed in Chinget al.[1]. The customers’ behavior is modelled by using a Markov chain and we aim at maximizing the expected profit using stochastic dynamic programming. We find that a multi-period promotion strategy is better than the strategy of applying several single-period promotions in our tested examples.
stochastic dynamic programming (SDP) is the method most extensively adopted to design release policies for water reservoir networks. However, it suffers of the well known “curse of dimensionality”, which actually li...
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stochastic dynamic programming (SDP) is the method most extensively adopted to design release policies for water reservoir networks. However, it suffers of the well known “curse of dimensionality”, which actually limits its applicability to small reservoir networks. In this paper we present an on-line approach to policy design that not only constitutes a viable alternative to overcome the SDP limits, but can also be used with an inflow predictor to improve the performance of SDP-based off-line policies. This latter possibility is explored and discussed through a real world case study.
We study solving large stochastic dynamic programming problems with simulation by using Blackwell’s approachability theorem to provide a rule of generating a (history-dependent) stochastic nonstationary policy from a...
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We study solving large stochastic dynamic programming problems with simulation by using Blackwell’s approachability theorem to provide a rule of generating a (history-dependent) stochastic nonstationary policy from a given finite set of policies whose performance is asymptotically not worse than any policy in the set by a given error. We provide an analysis for almost sure convergence with an exponentially fast convergence rate.
Management of Canada geese (Branta canadensis) can be a balance between providing sustained harvest opportunity while not allowing populations to become overabundant and cause damage. in this paper, we focus on the At...
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Management of Canada geese (Branta canadensis) can be a balance between providing sustained harvest opportunity while not allowing populations to become overabundant and cause damage. in this paper, we focus on the Atlantic population of Canada geese and use stochastic dynamic programming to determine the optimal harvest strategy over a range of plausible models for population dynamics. There is evidence to suggest that the population exhibits significant age structure, and it is possible to reconstruct age structure from surveys. Consequently the harvest strategy is a function of the age composition, as well as the abundance, of the population. The objective is to maximize harvest while maintaining the number of breeding adults in the population between specified upper and lower limits. In addition, the total harvest capacity is limited and there is uncertainty about the strength of density-dependence. We find that under a density-independent model, harvest is maximized by maintaining the breeding population at the highest acceptable abundance. However if harvest capacity is limited, then the optimal long-term breeding population size is lower than the highest acceptable level, to reduce the risk of the population growing to an unacceptably large size. Under the proposed density-dependent model, harvest is maximized by maintaining the breeding population at an intermediate level between the bounds on acceptable population size;limits to harvest capacity have little effect on the optimal long-term population size. It is clear that the strength of density-dependence and constraints on harvest significantly affect the optimal harvest strategy for this population. Model discrimination might be achieved in the long term, while continuing to meet management goals, by adopting an adaptive management strategy. (c) 2006 Elsevier B.V. All rights reserved.
We present a tactical decision model for order acceptance and capacity planning that maximizes the expected profits from accepted orders, allowing for aggregate regular as well as nonregular capacity. The stream of in...
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We present a tactical decision model for order acceptance and capacity planning that maximizes the expected profits from accepted orders, allowing for aggregate regular as well as nonregular capacity. The stream of incoming order arrivals is the main source of uncertainty in dynamic order acceptance and the company only has forecasts of the main properties of the future incoming projects. Project proposals arrive sequentially with deterministic interarrival times and a decision on order acceptance and capacity planning needs to be made each time a proposal arrives and its project characteristics are revealed. We apply stochastic dynamic programming to determine a profit threshold for the accept/reject decision as well as to deterministically allocate a single bottleneck resource to the accepted projects, both with an eye on maximizing the expected revenues within the problem horizon. We derive a number of managerial insights based on an analysis of the influence of project and environmental characteristics on optimal project selection and aggregate capacity usage. (c) 2007 Wiley Periodicals, Inc.
We consider the optimal sensor scheduling problem formulated as a partially observed Markov decision process (POMDP). Due to operational constraints, at each time instant, the scheduler can dynamically select one out ...
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We consider the optimal sensor scheduling problem formulated as a partially observed Markov decision process (POMDP). Due to operational constraints, at each time instant, the scheduler can dynamically select one out of a finite number of sensors and record a noisy measurement of an underlying Markov chain. The aim is to compute the optimal measurement scheduling policy, so as to minimize a cost function comprising of estimation errors and measurement costs. The formulation results in a nonstandard POMDP that is nonlinear in the information state. We give sufficient conditions on the cost function, dynamics of the Markov chain and observation probabilities so that the optimal scheduling policy has a threshold structure with respect to a monotone likelihood ratio (MLR) ordering. As a result, the computational complexity of implementing the optimal scheduling policy is inexpensive. We then present stochastic approximation algorithms for estimating the best linear MLR order threshold policy.
Many interacting predator-prey populations have a natural tendency to exhibit persistent limit-cycles or damped oscillations, especially in the presence of environmental stochasticity. The restriction of populations i...
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Many interacting predator-prey populations have a natural tendency to exhibit persistent limit-cycles or damped oscillations, especially in the presence of environmental stochasticity. The restriction of populations into small conservation reserves reduces the scale of ecosystems, and can induce cycling in previously stable predator-prey relationships. During the course of these cycles, the abundance of both predator and prey regularly decrease to low levels. At these times, environmental and demographic stochasticity may cause the extinction of one of the populations. Could culling one or both species at critical times reduce their probability of extinction? we use stochastic dynamic programming to determine the optimal management strategy for oscillation-prone species pairs. Remarkably, if the interventions are enacted at the appropriate time, infrequent culling of a small number of individuals significantly reduces the probability of extinction of the predator. Our approach can be applied to many different ecosystems, and can incorporate more complex system dynamics without a significant increase in computational time. (c) 2006 Elsevier B.V. All rights reserved.
Translocation is a useful management option for conservation of threatened animal species. It can be used to increase the range of a species, augment the numbers in a critical population, or establish new populations ...
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Translocation is a useful management option for conservation of threatened animal species. It can be used to increase the range of a species, augment the numbers in a critical population, or establish new populations and hence spread the risk of extinction through local catastrophes. As it is an important and expensive conservation tool, translocation management decisions must be carefully considered, with the objective of the translocation project in mind. By analysing the translocation problem within a decision-theory framework, we find optimal management decisions that are rational and transparent. We illustrate our approach using a case study of the bridled nailtail wallaby (Onychoyalea fraenata). Our particular translocation question is: if we have a set number of wallabies to translocate in each time period and two translocation sites, how many wallabies should we put at each site given the state of each population to maximise the benefit to the species? We model the translocated populations with first-order Markov chain stochastic population models, and use stochastic dynamic programming to determine the optimal management decisions. We look at two sites with different growth rates - one increasing and one decreasing - and compare the optimal strategies for two different objective functions. The first is a long-term persistence objective function, which maximises the persistence of translocated populations a large number of time steps after the end of the translocation program. The second maximises total population size at the end of the translocation program. Although these objective functions are similar, they generate surprisingly different optimal translocation strategies. When maximising the long-term persistence of the translocated populations, translocation decisions are not important as long as an increasing population is established. This indicates that site quality - rather than the number and timing of translocations - primarily determines the long
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