A decision theory framework can be a powerful technique to derive optimal management decisions for endangered species. We built a spatially realistic stochastic metapopulation model for the Mount Lofty Ranges Southern...
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A decision theory framework can be a powerful technique to derive optimal management decisions for endangered species. We built a spatially realistic stochastic metapopulation model for the Mount Lofty Ranges Southern Emu-wren (Stipiturus malachurus intermedius), a critically endangered Australian bird. Using diserete-time Markov,chains to describe the dynamics of a metapopulation and stochastic dynamic programming (SDP) to find optimal solutions, we evaluated the following different management decisions: enlarging existing patches, linking patches via corridors, and creating a new patch. This is the first application of SDP to optimal landscape reconstruction and one of the few times that landscape reconstruction dynamics have been integrated with population dynamics. SDP is a powerful tool that has advantages over standard Monte Carlo simulation methods because it can give the exact optimal strategy for every landscape configuration (combination of patch areas and presence of corridors) and pattern of metapopulation occupancy, as well as a trajectory of strategies. It is useful when a sequence of management actions can be performed over a given time horizon, as is the case for many endangered species recovery programs, where only fixed amounts of resources are available in each time step. However, it is generally limited by computational constraints to rather small networks of patches. The model shows that optimal metapopulation, management decisions depend greatly on the current state of the metapopulation,. and there is no strategy that is universally the best. The extinction probability over 30 yr for the optimal state-dependent management actions is 50-80% better than no management, whereas the best fixed state-independent sets of strategies are only 30% better than no management. This highlights the advantages of using a decision theory tool to investigate conservation strategies for metapopulations. It is clear from these results that the sequence of managem
P>1. When managing endangered species the consequences of making a poor decision can be extinction. To make a good decision, we must account for the stochasticdynamic of the population over time. To this end stoch...
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P>1. When managing endangered species the consequences of making a poor decision can be extinction. To make a good decision, we must account for the stochasticdynamic of the population over time. To this end stochastic dynamic programming (SDP) has become the most widely used tool to calculate the optimal policy to manage a population over time and under uncertainty. 2. However, as a result of its prohibitive computational complexity, SDP has been limited to solving small dimension problems, which results in SDP models that are either oversimplified or approximated using greedy heuristics that only consider the immediate rewards of an action. 3. We present a heuristic sampling (HS) method that approximates the optimal policy for any starting state. The method is attractive for problems with large state spaces as the running time is independent of the size of the problem state space and improves with time. 4. We demonstrate that the HS method out-performs a commonly used greedy heuristic and can quickly solve a problem with 33 million states. This is roughly 3 orders of magnitude larger than the largest problems that can currently be solved with SDP methods. 5. We found that HS out-performs greedy heuristics and can give near-optimal policies in shorter timeframes than SDP. HS can solve problems with state spaces that are too large to optimize with SDP. Where the state space size precludes SDP, we argue that HS is the best technique.
Organisms are constantly making tradeoffs. These tradeoffs may be behavioural (e.g. whether to focus on foraging or predator avoidance) or physiological (e.g. whether to allocate energy to reproduction or growth). Sim...
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Organisms are constantly making tradeoffs. These tradeoffs may be behavioural (e.g. whether to focus on foraging or predator avoidance) or physiological (e.g. whether to allocate energy to reproduction or growth). Similarly, wildlife and fishery managers must make tradeoffs while striving for conservation or economic goals (e.g. costs vs. rewards). stochastic dynamic programming (SDP) provides a powerful and flexible framework within which to explore these tradeoffs. A rich body of mathematical results on SDP exist but have received little attention in ecology and evolution. Using directed graphs - an intuitive visual model representation - we reformulated SDP models into matrix form. We synthesized relevant existing theoretical results which we then applied to two canonical SDP models in ecology and evolution. We applied these matrix methods to a simple illustrative patch choice example and an existing SDP model of parasitoid wasp behaviour. The proposed analytical matrix methods provide the same results as standard numerical methods as well as additional insights into the nature and quantity of other, nearly optimal, strategies, which we may also expect to observe in nature. The mathematical results highlighted in this work also explain qualitative aspects of model convergence. An added benefit of the proposed matrix notation is the resulting ease of implementation of Markov chain analysis (an exact solution for the realized states of an individual) rather than Monte Carlo simulations (the standard, approximate method). It also provides an independent validation method for other numerical methods, even in applications focused on short-term, non-stationary dynamics. These methods are useful for obtaining, interpreting, and further analysing model convergence to the optimal time-independent (i.e. stationary) decisions predicted by an SDP model. SDP is a powerful tool both for theoretical and applied ecology, and an understanding of the mathematical structure underly
This paper addresses an experimental validation of an energy management strategy on a parallel Hybrid Electric Vehicle (HEV). The strategy under consideration is based on stochastic dynamic programming. The control la...
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This paper addresses an experimental validation of an energy management strategy on a parallel Hybrid Electric Vehicle (HEV). The strategy under consideration is based on stochastic dynamic programming. The control law (determining the torque split between the engine and the motor) is computed off-line by solving an infinite horizon optimization problem. It results in a time-invariant state feedback controller function of vehicle acceleration and velocity, battery state of charge and engine state. This controller is first validated in simulation and then implemented in the vehicle electronic control unit. Experimental results highlight the good behavior of the control strategy. During a 35 km urban route, the strategy succeeds in regulating the battery state of charge and judiciously uses the powertrain.
A stochastic dynamic programming model is presented that supports and extends work on the reproductive performance of the !Kung Bushmen (Lee 1972;Blurton Jones and Sibly 1978;Blurton Jones 1986), proposing that !Kung ...
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A stochastic dynamic programming model is presented that supports and extends work on the reproductive performance of the !Kung Bushmen (Lee 1972;Blurton Jones and Sibly 1978;Blurton Jones 1986), proposing that !Kung women and their reproductive systems may be maximizing reproductive success, The stochastic dynamic programming approach allows the construction of a whole-life model where the physical/environmental constraints along with the uncertainty about future events !Kung women face when making reproductive choices can be explicitly built in, The model makes quantitative predictions for the optimal reproductive strategy assuming !Kung women are maximizing expected lifetime reproduction (ELR) given the physical parameters of !Kung life, The model relies on data gathered from the works cited above and some considerations from simple probability theory. The model predictions for optimal birth spacing match the !Kung reproductive data very well and support earlier findings (Blurton Jones and Sibly;Blurton Jones 1986), The utility of the dynamic modeling approach is illustrated when the effects of varying certain model parameters are investigated, By including the effect of the mother's mortality, which was not Included in the Blurton Jones and Sibly (1978) analysis, the model allows for further exploration of the application of an adaptive approach to human reproductive performance, By adding some considerations about the risks of childbirth for the mother the model not only predicts optimal birth spacing, which is site specific, but also predicts the optimal time for a woman to begin and cease having children, These predictions coincide with menarche and menopause and shed light on their possible adaptive value.
Air conditioning (AC) system, as a main electricity consumption unit among the Electric vehicle (EV) auxiliary devices, has significant effects on the electricity efficiency of EVs. An energy management strategy is cr...
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Air conditioning (AC) system, as a main electricity consumption unit among the Electric vehicle (EV) auxiliary devices, has significant effects on the electricity efficiency of EVs. An energy management strategy is critical to distribute the energy more reasonable and extend the driving range. In this paper, a stochastic dynamic programming (SDP) control strategy is used to optimize the electricity consumption of AC system. To study the relationship between solar radiation and electricity saving of AC system, a thermal model of the cabin and a mathematical model of AC system have been built. The electricity consumption using SDP algorithm is 10.35% less than using a rule-based controller. Additionally, the fluctuation of cabin temperature is considerably reduced with an AC system controlled by SDP.
1. Under increasing environmental and financial constraints, ecologists are faced with making decisions about dynamic and uncertain biological systems. To do so, stochastic dynamic programming (SDP) is the most releva...
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1. Under increasing environmental and financial constraints, ecologists are faced with making decisions about dynamic and uncertain biological systems. To do so, stochastic dynamic programming (SDP) is the most relevant tool for determining an optimal sequence of decisions over time. 2. Despite an increasing number of applications in ecology, SDP still suffers from a lack of widespread understanding. The required mathematical and programming knowledge as well as the absence of introductory material provide plausible explanations for this. 3. Here, we fill this gap by explaining the main concepts of SDP and providing useful guidelines to implement this technique, including R code. 4. We illustrate each step of SDP required to derive an optimal strategy using a wildlife management problem of the French wolf population. 5. stochastic dynamic programming is a powerful technique to make decisions in presence of uncertainty about biological stochastic systems changing through time. We hope this review will provide an entry point into the technical literature about SDP and will improve its application in ecology.
It is currently observed that rapid changes in generation and consumption can significantly impact the stability of a power system. In this light, this paper proposes a nonlinear feedback strategy for energy storage s...
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ISBN:
(纸本)9781665435970
It is currently observed that rapid changes in generation and consumption can significantly impact the stability of a power system. In this light, this paper proposes a nonlinear feedback strategy for energy storage systems which operates under uncertainty conditions, and does not require statistical representations of future signals. We employ stochastic dynamic programming (SDP) to derive the nonlinear feedback policy and then verify its stability properties using a Lyapunov-based analysis. A central challenge in such problems arises due to the calculation of two-dimensional value functions at each time instant, which requires considerable computational resources. To address this challenge, we consider a quadratic cost function and model the load power as a first-order auto-regressive stochastic process. We show that these considerations help solve the optimal control problem using SDP, and evaluate a feedback policy which is sub-optimal and stable. Numerical experiments reveal that this proposed feedback policy always keeps the stored energy within bounds, and allows it to follow the optimal path.
In this paper, we develop a framework to analyze stochasticdynamic optimization problems in discrete time. We obtain new results about the existence and uniqueness of solutions to the Bellman equation through a notio...
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In this paper, we develop a framework to analyze stochasticdynamic optimization problems in discrete time. We obtain new results about the existence and uniqueness of solutions to the Bellman equation through a notion of Banach contractions that generalizes known results for Banach and local contractions. We apply the results obtained to an endogenous growth model and compare our approach with other well-known methods, such as the weighted contraction method, countable local contractions, and the Q-transform.
Scheduling a residential building short-term to optimize the electricity bill can be difficult with the inclusion of capacity-based grid tariffs. Scheduling the building based on a proposed measured-peak (MP) grid tar...
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ISBN:
(纸本)9781728128221
Scheduling a residential building short-term to optimize the electricity bill can be difficult with the inclusion of capacity-based grid tariffs. Scheduling the building based on a proposed measured-peak (MP) grid tariff, which is a cost based on the highest peak power over a period, requires the user to consider the impact the current decision-making has in the future. Therefore, the authors propose a mathematical model using stochastic dynamic programming (SDP) that tries to represent the long-term impact of current decision-making. The SDP algorithm calculates non-linear expected future cost curves (EFCC) for the building based on the peak power backwards for each day over a month. The uncertainty in load demand and weather are considered using a discrete Markov chain setup. The model is applied to a case study for a Norwegian building with smart control of flexible loads, and compared against methods where the MP grid tariff is not accurately represented, and where the user has perfect information of the whole month. The results showed that the SDP algorithm performs 03 % better than a scenario with no accurate way of presenting future impacts, and performs 3.6 % worse compared to a scenario where the user had perfect information.
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