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CONSERVATION SCIENCE AND PRACTICE 2020年第4期2卷 1-13页

作者： O'Donnell, Katherine M. Fackler, Paul L. Johnson, Fred A. Bonneau, Mathieu N. Martin, Julien Walls, Susan C. US Geol Survey Wetland & Aquat Res Ctr 7920 NW 71st St Gainesville FL 32653 USA North Carolina State Univ Dept Agr & Resource Econ Raleigh NC USA Univ Florida Dept Wildlife Ecol & Conservat Gainesville FL USA US Geol Survey St Petersburg Coastal & Marine Sci Ctr St Petersburg FL USA INRA Unite Recherches Zootech URZ F-97170 Petit Bourg Guadeloupe France

Managing spatially structured populations of imperiled species presents many challenges. Spatial structure can make it difficult to predict population responses to potential recovery activities, and learning through experimentation may not be advised if it could harm threatened populations. Adaptive management provides an appealing framework when experimentation is considered too risky or time consuming;we used such an approach for imperiled flatwoods salamanders at a Florida wildlife refuge. We represented this metapopulation with category count models and used stochastic dynamic programming to identify optimal decision policies that weighed trade-offs between metapopulation persistence and management costs. We defined possible wetland categories in terms of habitat suitability and occupancy, specified category-specific management actions, and identified transition probabilities via expert elicitation for two management strategies: "future" status quo (FSQ;frequent growing-season burns) and extra management actions (EMA;restoration, translocation, head-starting). We simulated metapopulation dynamics using the resulting optimal management policy and found that under model FSQ, occupancy steadily declined over time, indicating that populations would rapidly become extirpated;with model EMA, occupancy remained stable, suggesting that populations would persist only if additional actions are applied and are effective. This approach can be used to identify optimal solutions while accounting for uncertainty and considering both habitat and population dynamics, and to help managers make conservation decisions for populations at imminent risk of extinction.

关键词： adaptive management Ambystoma amphibian decision analysis endangered species flatwoods salamander Markov decision process stochastic dynamic programming

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State-dependent mortality can enhance behavioral unpredictability

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BMC ECOLOGY 2020年第1期20卷 34-34页

作者： Okuyama, Toshinori Natl Taiwan Univ Dept Entomol 1Sec 4Roosevelt Rd Taipei 106 Taiwan

Background Although behavioral unpredictability is widely described within-individual variability in behavior, its adaptive significance is little understood. Using a dynamic state variable model, this study investigated the conditions under which behavioral unpredictability (a component of within-individual variability) in foraging behavior is advantageous. The model considers a situation in which a forager forages for a fixed period, represented by discrete time steps. The outcome of foraging may change the level of a state (e.g., size and fat storage) of the forager at each time step, and variability in the foraging outcome is assumed to be positively correlated with behavioral unpredictability. The probability of death at each time step is influenced by the state at the same time step. Reproduction occurs after all the foraging steps and is influenced by the state level of a forager at the time of reproduction. According to the expected utility hypothesis, the relationship (e.g., curvature) between the state and fitness will determine the role of behavioral unpredictability. In the model, the relationship was obtained by using the backward iteration method for each foraging time step. Results State-dependent mortality adds curvature to the relationship between the state and fitness, which makes the effect of behavioral unpredictability on fitness either positive or negative. This conclusion holds for any state-dependent mortality (i.e., as long as mortality is not independent of the state factor). Given that state-dependent mortality is commonly described, conditions that benefit behavioral unpredictability are likely also common. Conclusions When mortality depends on a state that is influenced by behavior, conditions that favor behavioral unpredictability may become common. How behavioral unpredictability influences the variability of behavioral outcomes is as important as how it influences the expectation of behavioral outcomes when studying the adaptive signi

关键词： Within-individual variation Foraging stochastic dynamic programming Risk-sensitivity

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Robust optimal reinsurance-investment strategy with price jumps and correlated claims

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INSURANCE MATHEMATICS & ECONOMICS 2020年 92卷 27-46页

作者： Chen, Zhiping Yang, Peng Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Peoples R China Xian Int Acad Math & Math Technol Ctr Optimizat Tech & Quantitat Finance Xian 710049 Shaanxi Peoples R China Xijing Univ Sch Sci Xian 710123 Peoples R China

This paper considers the robust optimal reinsurance-investment strategy selection problem with price jumps and correlated claims for an ambiguity-averse insurer (AAI). The correlated claims mean that future claims are correlated with historical claims, which is measured by an extrapolative bias. In our model, the AAI transfers part of the risk due to insurance claims via reinsurance and invests the surplus in a financial market consisting of a risk-free asset and a risky asset whose price is described by a jump-diffusion model. Under the criterion of maximizing the expected utility of terminal wealth, we obtain closed-form solutions for the robust optimal reinsurance-investment strategy and the corresponding value function by using the stochastic dynamic programming approach. In order to examine the influence of investment risk on the insurer's investment behavior, we further study the time-consistent reinsurance-investment strategy under the mean-variance framework and also obtain the explicit solution. Furthermore, we examine the relationship among the optimal reinsurance-investment strategies of the AAI under three typical cases. A series of numerical experiments are carried out to illustrate how the robust optimal reinsurance-investment strategy varies with model parameters, and result analyses reveal some interesting phenomena and provide useful guidances for reinsurance and investment in reality. (c) 2020 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Reinsurance-investment strategy Robust optimal Ambiguity Utility maximization Time consistency stochastic dynamic programming

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Decision analysis for greater insights into the development and evaluation of Chinook salmon restoration strategies in California's Central Valley

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RESTORATION ECOLOGY 2020年第6期28卷 1596-1609页

作者： Peterson, James T. Duarte, Adam Oregon State Univ Dept Fisheries & Wildlife Oregon Cooperat Fish & Wildlife Res Unit US Geol Survey 104 Nash Hall Corvallis OR 97331 USA Oregon State Univ Dept Fisheries & Wildlife Oregon Cooperat Fish & Wildlife Res Unit 104 Nash Hall Corvallis OR 97331 USA

Considerable resources have been invested in ecological restoration projects across the globe to restore ecosystem integrity. Restoration strategies are often diverse and have been met with mixed success. In this article, we describe the Chinook salmon (Oncorhynchus tshawytscha) decision-support models developed by the Central Valley Project Improvement Act Science Integration Team as part of a larger structured decision-making effort aimed at maximizing natural adult production of Chinook salmon in California's Central Valley, the United States. We then describe the decision-analytic tools the stakeholder group used to solve the models and explore model results, including stochastic dynamic programming, forward simulation, proportional scoring, relative loss, expected value of perfect information, response profile analyses, and indifference curves. Using these tools, the stakeholder group was able to develop and evaluate restoration strategies for multiple Chinook salmon runs simultaneously, a first for the restoration program. We found that actions targeted at one run were detrimental to others, which was unexpected. Furthermore, information uncovered during this process was used to direct efforts towards targeted research/monitoring to reduce critical uncertainties in salmon demographic rates and make better restoration decisions moving forward. The decision sciences have established a wide range of analytical tools and approaches to simplify complex problems into key components, and we believe the concepts described in this article are of great interest and can be applied by many restoration practitioners that undoubtedly face similar difficulties when implementing restoration strategies for complex systems.

关键词： decision-support model habitat restoration Oncorhynchus tshawytscha stochastic dynamic programming structured decision-making value of perfect information

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Inventory Management under Storage and Order Restrictions

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PRODUCTION AND OPERATIONS MANAGEMENT 2020年第1期29卷 101-117页

作者： Sharma, Shrutivandana Abouee-Mehrizi, Hossein Sartor, Giorgio Singapore Univ Technol & Design Engn Syst & Design 8 Somapah Rd Singapore 487372 Singapore Univ Waterloo Dept Management Sci 200 Univ Ave W Waterloo ON N2L 3G1 Canada SINTEF Digital Dept Math & Cybernet Forskningsveien 1 N-0373 Oslo Norway

Inventory management in most practical settings faces challenges due to various restrictions on storage and replenishment of products. These restrictions may be posed by budget availability, different production/supply schedules for different products, and limited storage space shared by a number of products-very common in retail, food, and the pharmaceutical industry. Motivated by this, we investigate in this study how simultaneous restrictions on shared storage capacity and product-specific order capacities impact optimal replenishments in a multi-product system. We formulate the inventory replenishment problem as a multi-period stochastic dynamic program, where products face stochastic demand with general distributions and excess demand is lost or fulfilled by emergency orders. We first fully characterize the optimal replenishment policy for two-product systems, and provide a methodology to compute optimal replenishment quantities, which can be described as a dynamic priority-based replenishment rule. Our results show that for each product, the optimal replenishment priority as well as quantity depends on the inventory levels of both products and all available capacities. More interestingly, the results show that capacity restrictions can flip the stocking priorities of products. Based on the optimal policy for two-product systems, we develop a heuristic for multi-product systems whose complexity scales linearly with the number of products. Under moderate storage capacities, our heuristic significantly outperforms the naive heuristics that ignore dynamic priority assignment, and closely captures the benefits of the optimal policy for systems with large number of products.

关键词： inventory management storage capacity order capacity optimal replenishment stochastic dynamic programming

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Application of artificial neural network for optimal operation of a multi-purpose multi-reservoir system, I: initial solution and selection of input variables

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SUSTAINABLE WATER RESOURCES MANAGEMENT 2020年第4期6卷 1-13页

作者： Shaikh, Safayat Ali Aliah Univ Dept Civil Engn Kolkata India

In this two-part series research paper, the effect of different input variables for optimal operation of a multipurpose multi-reservoir system using artificial neural network (ANN) models has been thoroughly investigated. As an alternative to multi-reservoir stochastic model, dynamic programming-based ANN models are developed. Due to nonavailability of any specific criterion to select the number and type of input variables for such ANN model, an exhaustive study with various combinations of input variables for single-reservoir ANN models is done. Damodar Valley, a multi-purpose multi-reservoir system in India, is used for this study. Results obtained from discrete dynamic programming, an optimization technique, are used as training data for ANN models. With different combinations of input data, five types of ANN models are developed. ANN models are simulated with generated inflow sequence as well as different types of observed historical inflow sequences. For each simulation, 240 monthly networks are trained and stored. Four different stochastic dynamic programming (SDP) models of the system are developed considering one reservoir at a time approach. Linear regression analysis is performed between the results obtained from ANN models with results of the best SDP model. Objective function values as well as different regression parameters are compared to select preferred ANN model and its input variables.

关键词： Artificial neural network Discrete dynamic programming stochastic dynamic programming Reservoir operation

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A stochastic Single Vehicle Routing Problem with a Predefined Sequence of Customers and Collection of Two Similar Materials

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METHODOLOGY AND COMPUTING IN APPLIED PROBABILITY 2020年第4期22卷 1559-1582页

作者： Kyriakidis, Epaminondas G. Dimitrakos, Theodosis D. Karamatsoukis, Constantinos C. Athens Univ Econ & Business Dept Stat Patiss 76 Athens 10434 Greece Univ Aegean Dept Math Samos 83200 Greece Hellen Mil Acad Dept Mil Sci Vari 16673 Attica Greece

We suppose that a vehicle visits N ordered customers in order to collect from them two similar but not identical materials. The actual quantity and the actual type of material that each customer possesses become known only when the vehicle arrives at the customer's location. It is assumed that the vehicle has two compartments. We name these compartments, Compartment 1 and Compartment 2. It is assumed that Compartment 1 is suitable for loading Material 1 and Compartment 2 is suitable for loading Material 2. However it is permitted to load items of Material 1 into Compartment 2 and items of Material 2 into Compartment 1. These actions cause extra costs that are due to extra labor. It is permissible for the vehicle to interrupt its route and go to the depot to unload the items of both materials. The costs for travelling from each customer to the next one and the costs for travelling from each customer to the depot are known. The objective is to find the routing strategy that minimizes the total expected cost among all possible strategies for servicing all customers. A dynamic programming algorithm is designed for the determination of the routing strategy that minimizes the total expected cost among all possible strategies. The structure of optimal routing strategy is characterized by a set of critical numbers for each customer.

关键词： stochastic dynamic programming Vehicle routing problem

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Feeder Ship Size Optimization in Maritime Markets with Uncertainty

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ASIA-PACIFIC JOURNAL OF OPERATIONAL RESEARCH 2021年第3期38卷 2040014-2040014页

作者： Hu, Hongtao Mo, Jiao Huang, Lufei Shanghai Maritime Univ Logist Engn Coll Shanghai 201306 Peoples R China Shanghai Maritime Univ Inst Logist Sci & Engn Shanghai 201306 Peoples R China Shanghai Univ Sch Management Shanghai 200444 Peoples R China

This paper considers the ship size optimization problem for a liner shipping company that provides feeder service between one hub port and one feeder port. In the maritime market with uncertainty, this problem becomes more challenging. This research first analyzes the decision behaviors of the shipping company. Then, a stochastic dynamic programming method is proposed to calculate the expected total volume of containers transported within the planning horizon. Using the calculated volumes as input parameters calculate the profit of each ship sizes and then determine the suitable ship size for the feeder route. Numerical experiments are performed to validate the effectiveness of the proposed method and the efficiency of the proposed algorithm.

关键词： Uncertain demands allowable due time of cargoes feeder containership size optimization stochastic dynamic programming

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Pricing electricity in constrained networks dominated by stochastic renewable generation and electric energy storage

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ELECTRIC POWER SYSTEMS RESEARCH 2021年 197卷 107169-107169页

作者： Aaslid, Per Korpas, Magnus Belsnes, Michael M. Fosso, Olav B. SINTEF Energy Res Trondheim Norway Norwegian Univ Sci & Technol Dept Elect Power Engn Trondheim Norway

This paper studies the electricity price formation in a competitive market when introducing generation from variable renewable energy technologies with zero marginal cost and electric energy storage systems. A power system is analyzed with a stochastic optimization model combining multi-period optimal power flow with stochastic dynamic programming. The results illustrate how variable renewable energy, in this case solar photovoltaic generation, displaces some of the expensive thermal generation and reduces the price. Electric energy storage will reduce the price variations caused by the variable renewable generation and demand as the time with price cap and zero price is reduced. In systems with only variable renewable generation and energy storage, the price will be set by the probability of scarcity similar to the price formation in hydro power dominated systems. The price will indicated the future cost of scarcity as a stochastic expectation value. This paper assumes that the demand is inflexible. However, the resulting electricity prices will remunerate provision of flexibility, which in turn will contribute to securing the supply and reducing the price volatility.

关键词： Electricity price Variable renewable energy Electric energy storage stochastic dynamic programming Multi-period optimal power flow Splines Infinite horizon

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A stochastic operational planning model for a zero emission building with emission compensation

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APPLIED ENERGY 2021年 302卷 117415-117415页

作者： Thorvaldsen, Kasper Emil Korpas, Magnus Lindberg, Karen Byskov Farahmand, Hossein Norwegian Univ Sci & Technol Dept Elect Power Engn Trondheim Norway SINTEF Community Oslo Norway

The primary objective of Zero Emission Buildings (ZEBs) is to achieve net zero emission over the buildings' lifetime. To achieve this goal, accurate cost-effective emission compensation is needed during the operational phase. This paper presents a stochastic planning model comprising an emission inventory for the operation of ZEBs. The operational planning methodology uses stochastic dynamic programming (SDP) to analyze and represent the expected future cost curve (EFCC) for operation based on the electricity price and accumulated CO2eq-inventory during the year. Failing to compensate for net zero emission makes the leftover amount subject to a penalty cost at the end of the year. This renders the overall problem multi-objective optimization including emission compensation and cost of operation. The model is applied to a case study of a Norwegian building, tested for a range of penalty costs for leftover CO2eq-inventory. The results show that, for a ZEB, including emission compensation demonstrates a significant impact on the operation of the building. The penalty cost puts a limit on how much the operational cost increase for additional compensation should be, influencing the end CO2eq-inventory. Increasing penalty costs decreases the end inventory, and a penalty cost of 10 EUR/kgCO(2equ) resulted in zero emission. The case achieving zero emission had an operational cost increase of 4.8% compared to operating without a penalty cost. This shows the importance of accounting for emissions during the operation of a ZEB, and the value of having an operational strategy that presents the future impact of operation.

关键词： Operational planning stochastic dynamic programming Grid interaction Demand-side management Hourly CO2eq-intensity

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