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Comparison of risk-based optimization models for reservoir management

CANADIAN JOURNAL OF CIVIL ENGINEERING 2010年第1期37卷 112-124页

作者： Mahootchi, M. Ponnambalam, K. Tizhoosh, H. R. Univ Waterloo Dept Syst Design Engn Waterloo ON N2L 3G1 Canada Amirkabir Univ Technol Dept Ind Engn Tehran Iran

Risk minimization in stochastic systems is a challenging problem and this paper compares results of three different techniques in reservoir management. Two-stage stochastic programming (TSP) for maximizing expected benefits is a well-known method, Fletcher and Ponnambalam (FP) and Q-Learning are the two new methods in reservoir management, all of which can include risk minimization in the objective function. The water price uncertainties caused by deregulated markets are considered in addition to random inflows in optimization and simulation is used to compare the results and to develop a risk versus return trade-off curve. One of the contributions of this paper is to consider risk in the Q-Learning algorithm.

关键词： water reservoirs stochastic dynamic programming Q-Learning risk stochastic programming

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The value of precision feeding technologies for grow-finish swine

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LIVESTOCK SCIENCE 2010年第1-3期129卷 13-23页

作者： Niemi, Jarkko K. Sevon-Aimonen, Marja-Liisa Pietola, Kyosti Stalder, Kenneth J. MTT Agrifood Res Finland Econ Res FI-00410 Helsinki Finland MTT Agrifood Res Finland Biotechnol & Food Res FI-31600 Jokioinen Finland Iowa State Univ Dept Anim Sci Ames IA 50011 USA

Feed and piglet are the largest variable cost items and important factors affecting the profitability of grow-finish pork production in most pork producing countries world-wide. Although a pig's potential to utilize energy and protein depends on its weight, age and genetic line, grow-finish pigs are typically fed with only 1 to 4 diets during the grow-finish phases of production. The goal of this study is to examine whether there are differences in feeding patterns, the timing of slaughter and return on pig space unit between 1) multi-phase feeding in which the quantity and protein content of the feed and the timing of slaughter are optimized on a daily basis, and 2) two-phase feeding in which feed quantity and slaughter decision are optimized daily but feed composition is predetermined for a given weight range. A stochastic dynamic programming model was used to evaluate different scenarios. The model maximizes return on capital invested in production facilities, which is critical for the competitiveness of pork production. The bio-economic model explicitly characterizes the growth process of grow-finish pigs and optimizes the timing of slaughter and daily amounts of protein and energy used in the feed. The growth of lipid mass and protein mass in the pig in a grow-finish group are modelled separately. The results suggest that a switch from the two-phase feeding to the multi-phase feeding shifts feeding regimen towards increased protein inclusion in the diets. It increases annual return on the operation by (sic)1.35-(sic)1.88 per pig space unit. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Pig Phase feeding Soybean meal Barley stochastic dynamic programming

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Evaluation of Regulatory Impacts on dynamic Behavior of Investments in Electricity Markets: A New Hybrid DP/GAME Framework

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IEEE TRANSACTIONS ON POWER SYSTEMS 2010年第4期25卷 1978-1986页

作者： Barforoushi, Taghi Moghaddam, Mohsen P. Javidi, M. Hossein Sheikh-El-Eslami, Mohammad K. Tarbiat Modares Univ Dept Elect Engn Tehran Iran Ferdowsi Univ Mashhad Dept Elect Engn Mashhad Iran

A new hybrid framework based on game theory and dynamic programming (DP) with random demands and prices is proposed for studying the impacts of regulatory interventions on the dynamics of investment in power generation in electricity markets. In our approach, using Markov chains, the electric demand and growth of fuel prices have been modeled. DP has been used for solving the generation expansion planning (GEP) problem. Investment strategies of other investors in the market are modeled as constraints. The income of the investor is calculated by modeling strategic interactions among market players in the spot energy market. The Cournot game concept has been applied and the Nash equilibrium is calculated for each state and stage of DP. Simulation results confirm that the proposed framework is an appropriate decision-support tool that provides useful information about dynamics of investment.

关键词： Electricity markets game theory generation expansion planning investment dynamics stochastic dynamic programming

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Optimal replacement policies for dairy cows based on daily yield measurements

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JOURNAL OF DAIRY SCIENCE 2010年第1期93卷 75-92页

作者： Nielsen, L. R. Jorgensen, E. Kristensen, A. R. Ostergaard, S. Aarhus Univ Dept Business Studies DK-8210 Aarhus V Denmark Aarhus Univ Dept Genet & Biotechnol DK-8830 Tjele Denmark Univ Copenhagen Dept Large Anim Sci DK-1870 Frederiksberg C Denmark Aarhus Univ Dept Anim Hlth & Biosci DK-8830 Tjele Denmark

Markov decision processes (MDP) with finite state and action space have often been used to model sequential decision making over time in dairy herds. However, the length of each stage has been at least 1 mo, resulting in models that do not support decisions on a daily basis. The present paper describes the first step of developing an MDP model that can be integrated into a modern herd management system. A hierarchical MDP was formulated for the dairy cow replacement problem with stage lengths of 1 d. It can be used to assist the farmer in replacement decisions on a daily basis and is based on daily milk yield measurements that are available in modern milking systems. Bayesian updating was used to predict the performance of each cow in the herd and economic decisions were based on the prediction. Moreover, parameters in the model were estimated using data records of the specific herd under consideration. This includes herd-specific lactation curves.

关键词： hierarchical Markov decision process stochastic dynamic programming state space model herd management

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Should metapopulation restoration strategies increase patch area or number of patches?

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ECOLOGICAL APPLICATIONS 2010年第2期20卷 566-581页

作者： Nicol, Samuel C. Possingham, Hugh P. Univ Queensland Dept Math Brisbane Qld 4072 Australia Univ Queensland Sch Integrat Biol Brisbane Qld 4072 Australia

Managers of species that exist as metapopulations are faced with many decisions. In this paper we use a decision-theory framework to examine a fundamental management question: Should we focus on decreasing the local extinction probability of subpopulations by increasing the size of their patch, or should metapopulation viability be improved by constructing more patches? Using a spatially implicit stochastic metapopulation model and stochastic dynamic programming (SDP), we found the optimal solution to this problem for both the finite- and infinite-time horizon cases. We showed that the SDP solutions outperform a range of heuristic management strategies. The optimal strategy for a given parameter set depends heavily on metapopulation parameters, and it is difficult to make generalizations about the optimal restoration strategy a priori. Although heuristic strategies perform well in some cases, it is not possible to judge their performance until the SDP solution has been computed, and for this reason we advocate the use of SDP as a management tool in restoration. We demonstrate the use of SDP by deriving an optimal management strategy for a population of the Mount Lofty Ranges Southern Emu-wren (Stipituras malachurus intermedius).

关键词： conservation decision theory Fleurieu Peninsula, South Australia metapopulation Mount Lofty Ranges Southern Emu-wren optimal management patch dynamics restoration Stipiturus malachurus intermedius stochastic dynamic programming

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Adaptive management for eradication of exotic species

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POPULATION ECOLOGY 2010年第3期52卷 349-358页

作者： Kotani, Koji Kakinaka, Makoto Matsuda, Hiroyuki Int Univ Japan Grad Sch Int Relat Int Dev Program Niigata 9497277 Japan Yokohama Natl Univ Fac Environm & Informat Sci Hodogaya Ku Kanagawa 2408501 Japan

The problems of controlling exotic species have been acknowledged as serious threats to an indigenous ecosystem as well as to society. In response to these threats, various management programs of exotic species have been proposed for supporting eradication in many regions. Although eradication is regarded as the first-best solution, such attempts have been unsuccessful in reality due to mainly two factors: (1) stock-dependent catchability, and (2) uncertainties. This article demonstrates when to aim at eradication through addressing an optimal adaptive management strategy in the framework of a bio-economic model with the aforementioned factors. The study sets out that the sensitivity of catchability in response to a change in the existing stock determines whether or not aiming at eradication is justified. The results also show that process uncertainty associated with stock growth significantly affects the timing of removal actions for eradication, and an increase in the degree of uncertainty could help achieve eradication in a cost-effective manner if we optimally adapt our removal actions to the uncertainty.

关键词： Bio-economic model Exotic species management Process uncertainty stochastic dynamic programming Value function

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Active adaptive conservation of threatened species in the face of uncertainty

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ECOLOGICAL APPLICATIONS 2010年第5期20卷 1476-1489页

作者： McDonald-Madden, Eve Probert, William J. M. Hauser, Cindy E. Runge, Michael C. Possingham, Hugh P. Jones, Menna E. Moore, Joslin L. Rout, Tracy M. Vesk, Peter A. Wintle, Brendan A. CSIRO Sustainable Ecosyst St Lucia Qld 4067 Australia Univ Queensland Dept Math St Lucia Qld 4069 Australia Univ Melbourne Australian Ctr Excellence Risk Anal Melbourne Vic 3010 Australia US Geol Survey Patuxent Wildlife Res Ctr Laurel MD 20708 USA Univ Tasmania Sch Zool Hobart Tas 7001 Australia Dept Primary Ind & Water Hobart Tas 7001 Australia Univ Melbourne Ctr Appl Environm Decis Anal Sch Bot Parkville Vic 3010 Australia

Adaptive management has a long history in the natural resource management literature, but despite this, few practitioners have developed adaptive strategies to conserve threatened species. Active adaptive management provides a framework for valuing learning by measuring the degree to which it improves long-run management outcomes. The challenge of an active adaptive approach is to find the correct balance between gaining knowledge to improve management in the future and achieving the best short-term outcome based on current knowledge. We develop and analyze a framework for active adaptive management of a threatened species. Our case study concerns a novel facial tumor disease affecting the Australian threatened species Sarcophilus harrisii: the Tasmanian devil. We use stochastic dynamic programming with Bayesian updating to identify the management strategy that maximizes the Tasmanian devil population growth rate, taking into account improvements to management through learning to better understand disease latency and the relative effectiveness of three competing management options. Exactly which management action we choose each year is driven by the credibility of competing hypotheses about disease latency and by the population growth rate predicted by each hypothesis under the competing management actions. We discover that the optimal combination of management actions depends on the number of sites available and the time remaining to implement management. Our approach to active adaptive management provides a framework to identify the optimal amount of effort to invest in learning to achieve long-run conservation objectives.

关键词： active adaptive management Bayesian updating decision theory learning Markov decision process Sarcophilus harrisii stochastic dynamic programming Tasmania, Australia Tasmanian devil facial tumor disease

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Temporal Difference Learning Waveform Selection

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JOURNAL OF COMPUTERS 2010年第9期5卷 1394-1401页

作者： Wang, Bin Wang, Jinkuan Song, Xin Han, Yinghua Nrotheastern Univ Shenyang Liaoning Peoples R China Nrotheastern Univ Qinhuangdao Qinhuangdao Peoples R China

How to optimally decide or select the radar waveform for next transmission based on the observation of past radar returns is one of the important problems in cognitive radar. In this paper, stochastic dynamic programming model is proposed. Then temporal difference learning method is used to realize the adaptivity of waveform selection. The simulation results show that the uncertainty of state estimation using temporal difference learning is less than that using fixed waveform. Temporal difference learning method approaches the optimal waveform selection scheme but has lower computational cost. Finally, the whole paper is summarized.

关键词： waveform selection stochastic dynamic programming temporal difference learning cognitive radar

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A modeling framework for optimizing the flow-level scheduling with time-varying channels

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PERFORMANCE EVALUATION 2010年第11期67卷 1014-1029页

作者： Ayesta, Urtzi Erausqum, Martin Jacko, Peter BCAM Bilbao 48170 Spain IKERBASQUE Basque Fdn Sci Bilbao 48011 Spain Univ Basque Country UPV EHU Bilbao Spain

We introduce a comprehensive modeling framework for the problem of scheduling a finite number of finite-length jobs where the available service rate is time-varying The main motivation comes from wireless data networks where the service rate of each user varies randomly due to fading We employ recent advances on the restless bandit problem that allow us to obtain an opportunistic scheduling rule for the system without arrivals When the objective is to minimize the mean number of users in the system or to minimize the mean waiting time, we obtain a priority-based policy which we call the Potential Improvement (PI) rule, since the priority index equals the ratio between the current available service rate and the expected potential improvement of the service rate We also show that for certain objective functions the index rule takes the form of known opportunistic scheduling rules like Relatively Best (RB) or Proportionally Best" (PB) Thus our model provides a formal justification for the deployment of opportunistic scheduling rules in order to improve the flow-level performance in the presence of time-varying capacities We further analyze the performance of the PI rule in the presence of randomly arriving users When the service rates are constant PI is equivalent to the c mu-rule which is known to be optimal with any distribution of arrivals Using a recent characterization for the stability region of flow-level scheduling rules under random arrivals we show that PI achieves the maximum stability region We perform numerical experiments in a wide range of scenarios and compare the performance of PI with other popular disciplines like RB PB Score-Based (SB) and the c mu-rule Our results show that RB PB SB or the c mu-rule might outperform the others depending on the scenario, but regardless of this the performance of PI is always superior or equivalent to the best of these opportunistic rules (C) 2010 Elsevier B V All rights reserved

关键词： Multi-armed restless bandit problem Markov Decision Process stochastic dynamic programming Opportunistic scheduling Optimal scheduling c mu-rule Index policy

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Approximate dynamic programming approach for process control

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JOURNAL OF PROCESS CONTROL 2010年第9期20卷 1038-1048页

作者： Lee, Jay H. Wong, Weechin Georgia Inst Technol Sch Chem & Biomol Engn Atlanta GA 30332 USA

We assess the potentials of the approximate dynamic programming (ADP) approach for process control, especially as a method to complement the model predictive control (MPC) approach. In the artificial intelligence (AI) and operations research (OR) research communities, ADP has recently seen significant activities as an effective method for solving Markov decision processes (MDPs), which represent a type of multi-stage decision problems under uncertainty. Process control problems are similar to MDPs with the key difference being the continuous state and action spaces as opposed to discrete ones. In addition, unlike in other popular ADP application areas like robotics or games, in process control applications first and foremost concern should be on the safety and economics of the on-going operation rather than on efficient learning. We explore different options within ADP design, such as the pre-decision state vs. post-decision state value function, parametric vs. nonparametric value function approximator, batch-mode vs. continuous-mode learning, and exploration vs. robustness. We argue that ADP possesses great potentials, especially for obtaining effective control policies for stochastic constrained nonlinear or linear systems and continually improving them towards optimality. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： stochastic process control stochastic dynamic programming Approximate dynamic programming Dual control Constrained control

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