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The aim of this study is to estimate the optimal forest harvest considering economic and environmental objectives in an uneven-aged forest in the north of Iran. Required data were collected such as stumpage price, harvesting variable costs, growth, volume, basal area, sequestrated carbon, carbon dioxide storage, and price. The required input models for optimization were estimated using the collected data. A stochastic dynamic programming technique was used for optimization. The results indicated that the harvesting volume was higher when only the economic objective was considered instead of considering economic and environmental objectives. The results also indicated that the harvesting volume would occur at a higher level of volume per hectare if we only consider the environmental objective. The results showed that the expected present value was higher when economic and environmental objectives were considered simultaneously. The results indicated when interest rate increased, the harvesting time and the expected present value decreased. The results showed that the expected present value increases with increasing the risk. The results of this study can be a guideline for optimal forest management. [ABSTRACT FROM AUTHOR]

Informed conservation and management of wildlife require sufficient monitoring to understand population dynamics and to direct conservation actions. Because resources available for monitoring are limited, conservation practitioners must strive to make monitoring as cost‐effective as possible.Our focus was on assessing the value of monitoring to the adaptive harvest management (AHM) programme for pink‐footed geese Anser brachyrhynchus. We conducted a retrospective analysis to assess the costs and benefits of a capture–mark–resight (CMR) programme, a productivity survey and biannual population censuses. Using all available data, we fit an integrated population model (IPM) and assumed that inference derived from it represented the benchmark against which reduced monitoring was to be judged. We then fit IPMs to reduced sets of monitoring data and compared their estimates of demographic parameters and expected management performance against the benchmark IPM.Costs and the precision and accuracy of key demographic parameters decreased with the elimination of monitoring data. Eliminating the CMR programme, while maintaining other monitoring instruments, resulted in the greatest cost savings, usually with small effects on inferential reliability. Productivity surveys were also expensive and some reduction in survey effort may be warranted. The biannual censuses were inexpensive and generally increased inferential reliability.The expected performance of AHM strategies was surprisingly robust to a loss of monitoring data. We attribute this result to explicit consideration of parametric uncertainty in harvest‐strategy optimization and the fact that a broad range of population sizes is acceptable to stakeholders.Synthesis and applications. Our study suggests that existing or potential monitoring instruments for wildlife populations should be scrutinized as to their cost‐effectiveness for improving biological inference and management performance. Using Svalbard pink‐footed geese as a case study, we show that the loss of some existing monitoring instruments may not be as adverse as commonly assumed if data are jointly analysed in an IPM. Finally, regardless of the monitoring data available, we suggest that conservation strategies that explicitly account for uncertainty in demography are more likely to be successful than those that do not. [ABSTRACT FROM AUTHOR]

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Copyright of e & i Elektrotechnik und Informationstechnik is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The genealogy process is typically the most time‐consuming part of—and a limiting factor in the success of—forensic genetic genealogy, which is a new approach to solving violent crimes and identifying human remains. We formulate a stochastic dynamic program that—given the list of matches and their genetic distances to the unknown target—chooses the best decision at each point in time: which match to investigate (i.e., find its ancestors and look for most recent common ancestors between the match and the target), which set of potential most recent common ancestors to descend from (i.e., find its descendants, with the goal of identifying a marriage between the maternal and paternal sides of the target's family tree), or whether to terminate the investigation. The objective is to maximize the probability of finding the target minus a cost associated with the expected size of the final family tree. We estimate the parameters of our model using data from 17 cases (eight solved, nine unsolved) from the DNA Doe Project. We assess the Proposed Strategy using simulated versions of the 17 DNA Doe Project cases, and compare it to a Benchmark Strategy that ranks matches by their genetic distance to the target and only descends from known common ancestors between a pair of matches. The Proposed Strategy solves cases ≈10 − fold faster than the Benchmark Strategy, and does so by aggressively descending from a set of potential most recent common ancestors between the target and a match even when this set has a low probability of containing the correct most recent common ancestor. Our analysis provides a mathematical foundation for improving the genealogy process in forensic genetic genealogy. [ABSTRACT FROM AUTHOR]

This study investigates how imperfect advance load information (IALI) can improve profits and other operational indicators, such as empty movements, for a single-truck company. To analyze the value of IALI, we first develop a deterministic mathematical model. Then, we propose a stochastic dynamic programming approach that can utilize IALI. After designing a comprehensive set of experiments, we employ both models using a dynamic implementation mechanism to assess the benefits of using IALI. Our statistical analysis reveals that (1) utilizing IALI can significantly improve a single-truck company's profits, by as much as almost 30% on average, and (2) the impact of using IALI can be affected by other factors (e.g., network size). In another set of experiments, we examine the benefits of IALI in a new environment where there are two classes of shippers, high risk and low risk. The results suggest that the potential benefits can be even larger with two classes of shippers. Last, we collect data over two three-week periods for a single-truck company that operates in Ontario, Canada, and we apply our methods for evaluating the benefits of IALI. [ABSTRACT FROM AUTHOR]

Rational Water Resources Management requires effective collaboration between decision-makers involved in the operational management of water resources and scientists, who can allow them to operate in an informed manner through forecasting and decision-making tools. In this article, we show the potential benefits resulting from this collaboration through the description of the emblematic case of Lake Como. The article describes the real case of a collaborative experience between decision makers, who made an effort to highlight and clarify the real management problems to scientists, who in turn needed to understand all the facets of the decision-making process prior to formulating the problem in mathematical terms and incorporating the solution into a decision support system. The resulting tool, which makes extensive hidden use of probabilistic forecasts, stochastic optimization, and Bayesian decision techniques, resulted in a user-friendly environment. After six months of testing, the tool proved to be essential for decision-making and has been in use on a daily basis since 1997. [ABSTRACT FROM AUTHOR]

We propose and analyze a temporal concatenation heuristic for solving large-scale finite-horizon Markov decision processes (MDP), which divides the MDP into smaller sub-problems along the time horizon and generates an overall solution by simply concatenating the optimal solutions from these sub-problems. As a "black box" architecture, temporal concatenation works with a wide range of existing MDP algorithms. Our main results characterize the regret of temporal concatenation compared to the optimal solution. We provide upper bounds for general MDP instances, as well as a family of MDP instances in which the upper bounds are shown to be tight. Together, our results demonstrate temporal concatenation's potential of substantial speed-up at the expense of some performance degradation. [ABSTRACT FROM AUTHOR]

Stochastic dynamic programming (SDP) is an optimization technique used in the operation of reservoirs for many years. However, being an iterative method requiring considerable computational time, it is important to establish adequate convergence criterion for its most effective use. Based on two previous studies for the optimization of operations in one of the most important multi-reservoir systems in Mexico, this work uses SDP, centred on the interest in the convergence criterion used in the optimization process. In the first trial, following the recommendations in the literature consulted, the difference in the absolute value of two consecutive iterations was taken and compared against a set tolerance value and a discount factor. In the second trial, it was decided to take the squared difference of the two consecutive iterations. In each of the trials, the computational time taken to obtain the optimal operating policy was quantified, along with whether the optimal operating policy was obtained by meeting the convergence criterion or by reaching the maximum number of iterations. With each optimization policy, the operation of the system under study was simulated and four variables were taken as evaluators of the system behaviour. The results showed few differences in the two operation policies but notable differences in the computation time used in the optimization process, as well as in the fulfilment of the convergence criterion. [ABSTRACT FROM AUTHOR]

We study the optimal corporate policy of a risk-averse shareholder under leverage-dependent borrowing costs and other financial frictions. The firm's objective is to maximize the risk-adjusted shareholder value by co-optimizing investment, dividend, and debt policies considering endogenous (leverage-dependent) leveraging costs, tax shield, as well as costs of equity issuance and asset fire sale. The resulting multistage stochastic linear program model is efficiently solved by the Stochastic Dual Dynamic Programming algorithm. After certifying that the risk-neutral results are consistent with previous studies, our model helps to resolve the well-known low-leverage puzzle, a dissonance between the empirical and structural modeling literature in corporate finance. Our case study results show that risk-aversion combined with leverage-dependent borrowing cost can significantly reduce the optimal leverage ratio as well as the firm size without significantly compromising dividend payments. [ABSTRACT FROM AUTHOR]

This paper investigates a dual-channel retailer's inventory and pricing integrated decision-making problem over multiple periods. The quantity-sales mode is adopted in the online channel while the unit-sales mode is used offline. The customer demand of each sales channel is stochastic, and depends on visit costs, customer return costs, sales modes and pricing levels. To achieve the long-term profit maximization, the dual-channel retailer needs to optimize the pricing levels of both channels and the ordering quantity. For this problem, we first formulate a stochastic dynamic programming model, and then prove the joint concavity and the supermodularity of the objective function. With these properties, the optimality of the base-stock policy is proved, and the optimum price policy is characterized. Specifically, if the retailer's optimum stock is neither too low nor too high, both sales channels need to be operated simultaneously, otherwise, only operating one channel is optimal. The online price is invariant with the retailer's stock level while the offline price is decreasing in the retailer's stock level. Finally, numerical results show that the quantity-sales mode, dual-channel sales model, and the multi-period dynamic decisions could benefit the dual-channel retailer. In addition, influences of cost parameters on the total profit are verified numerically. [ABSTRACT FROM AUTHOR]

Hybrid green energy system, consisting of photovoltaic (PV), fuel cell and battery, receives wide attention because of its autonomy, flexibility and promising potential in accelerating the development of carbon neutrality in the field of power generation. However, the efficient power dispatching of the hybrid energy system is challenging due to the inevitable uncertainties of the solar energy. To this end, stochastic dynamic programming (SDP) is used in this paper to find the optimal solution to minimize the total fuel consumption during a 72-hour operating cycle, with constraints on the magnitude and rate of the fuel cell and battery operation, in which the stochastic characteristics of the solar power are described by Markov chain. The influence of sampling time and number of states on the solar prediction accuracy is discussed. For comparison, the traditional rule-based algorithm and dynamic programming (DP) algorithms are also utilized to describe and solve the power distribution problem, corresponding to different decision results in terms of how to distribute the energy flow for each time period. Numerical optimization results within the 72-hour period demonstrate that, the SDP has a 20.61% economy and 66.34% battery SOC improvement than that of rule-based algorithm, and in most uncertain cases, the SDP produces superior economic performance than those of both the rule-based algorithm and DP, benefited from the inclusion of the solar power probabilities into the optimization framework. The results of this paper lay a solid foundation for the efficient energy management of the hydrogen and solar hybrid energy system. The stochastic dynamic programming is used in this paper to deal with the uncertainties in solar energy and find the optimal energy scheduling of a hybrid fuel cell/PV/battery system. Numerical optimization results demonstrate that, in most uncertain cases, stochastic dynamic programming produces superior economic performance than those of both the rule-based and dynamic programming algorithm. The results lay a solid foundation for the efficient energy management of the hydrogen and solar hybrid energy system. [Display omitted] [ABSTRACT FROM AUTHOR]

We study the nonlinear newsvendor problem concerning goods of a non-discrete nature, and a class of stochastic dynamic programs with several application areas such as supply chain management and economics. The class is characterized by continuous state and action spaces, either convex or monotone cost functions that are accessed via value oracles, and affine transition functions. We establish that these problems cannot be approximated to any degree of either relative or additive error, regardless of the scheme used. To circumvent these hardness results, we generalize the concept of fully polynomial-time approximation scheme allowing arbitrarily small additive and multiplicative error at the same time, while requiring a polynomial running time in the input size and the error parameters. We develop approximation schemes of this type for the classes of problems mentioned above. In light of our hardness results, such approximation schemes are "best possible". A computational evaluation shows the promise of this approach. [ABSTRACT FROM AUTHOR]

We study an online linear programming (OLP) problem under a random input model in which the columns of the constraint matrix along with the corresponding coefficients in the objective function are independently and identically drawn from an unknown distribution and revealed sequentially over time. Virtually all existing online algorithms were based on learning the dual optimal solutions/prices of the linear programs (LPs), and their analyses were focused on the aggregate objective value and solving the packing LP, where all coefficients in the constraint matrix and objective are nonnegative. However, two major open questions were as follows. (i) Does the set of LP optimal dual prices learned in the existing algorithms converge to those of the "offline" LP? (ii) Could the results be extended to general LP problems where the coefficients can be either positive or negative? We resolve these two questions by establishing convergence results for the dual prices under moderate regularity conditions for general LP problems. Specifically, we identify an equivalent form of the dual problem that relates the dual LP with a sample average approximation to a stochastic program. Furthermore, we propose a new type of OLP algorithm, action-history-dependent learning algorithm, which improves the previous algorithm performances by taking into account the past input data and the past decisions/actions. We derive an O (log n log log n) regret bound (under a locally strong convexity and smoothness condition) for the proposed algorithm, against the O (n) bound for typical dual-price learning algorithms, where n is the number of decision variables. Numerical experiments demonstrate the effectiveness of the proposed algorithm and the action-history-dependent design. [ABSTRACT FROM AUTHOR]

Battery energy storage systems (BESSs) have the potential to reduce end users’ electricity bills by shifting their grid demand in response to price incentives. Some electricity retailers charge end users for their peak demand in the billing cycle regardless of the time of occurrence. Yet, using BESSs to reduce demand charges is challenging, since the net demand of end users can be highly uncertain due to low aggregation and on-site renewable generation. In this article, an approximate dynamic programming (ADP) methodology is developed to control a BESS to minimize an end user’s electricity bill, which includes both an energy charge and monthly peak demand charge. To address time-varying uncertainty, the net demand is modeled using a periodic autoregressive (PAR) model, which is then used to formulate a Markov Decision Process whose objective is to minimize the sum of energy, peak demand, and battery usage costs. A backward ADP strategy is developed which is enabled by new closed-form expressions for the probability distribution of the expected stage and tail costs when a radial basis function (RBF) value function approximation (VFA) is employed. The approach is applied to real net demand data from a small Australian residential community and compared to two benchmark policies: a lookup table VFA and a model predictive control (MPC) approach. The results show that the proposed approach reduces the average monthly peak demand by about 25% (yielding an 8% reduction in the electricity bill), whereas the best-performing benchmark policy reduced the peak demand by about 17%. [ABSTRACT FROM AUTHOR]

We used a stochastic dynamic programming (SDP) model to quantify the consequences of disturbance on pregnant western gray whales during one foraging season. The SDP model has a firm basis in bioenergetics, but detailed knowledge of minimum reproductive length of females (Lmin) and the relationship between length and reproductive success (Rfit) was lacking. We varied model assumptions to determine their effects on predictions of habitat use, proportion of animals disturbed, reproductive success, and the effects of disturbance. Smaller Lmin values led to higher predicted nearshore habitat use. Changes in Lmin and Rfit had little effect on predictions of the effect of disturbance. Reproductive success increased with increased Lmin and with higher probability of reproductive success by length. Multiple seismic surveys were conducted in 2015 off the northeast coast of Sakhalin Island, with concomitant benthic prey surveys, photo-identification studies, and whale distribution sampling, thus providing a unique opportunity to compare output from SDP models with empirical observations. SDP model predictions of reproductive success and habitat use were similar with and without acoustic disturbance, and SDP predictions of reproductive success and large-scale habitat use were generally similar to values and trends in the data. However, empirical estimates of the proportion of pregnant females nearshore were much higher than SDP model predictions (a large effect, measured by Cohen's d) during the first week, and the SDP model overestimated whale density in the south and underestimated density around the mouth of Piltun Bay. Such differences in nearshore habitat use would not affect SDP predictions of reproductive success or survival under the current seismic air gun disturbance scenario. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the farmer's risk income when using commodity futures, when price and output processes are randomly correlated and represented by jump-diffusion models. We evaluate the expected utility of the farmer's wealth and determine the optimal consumption rate and hedging position at each point in time given the harvest timing and state variables. We find a closed form for the optimal consumption and positioning rate in the case of an investor with CARA utility. This result (see Table 3.3) is a generalization of the result of Ho (J Financ 39:351–376, 1984), which considers the special case in which price and output are diffusion models. [ABSTRACT FROM AUTHOR]

Shellfish aquaculture producers in coastal systems are facing uncertain future growing conditions as climate change alters weather patterns and raises sea level. We examined expected mid-century (2059-2068) changes in aquaculture profitability from recent conditions by integrating models of climate change, estuarine hydrodynamics and biogeochemistry, oyster growth, oyster mortality, and economics, using the Chesapeake Bay, USA as a case study. We developed an economic stochastic dynamic programming (SDP) approach that generates optimal grower behavior to maximize profits under uncertainty by dynamically choosing planting density, replanting and mitigation use, in response to changing oyster stock status and water quality conditions. Separate models were developed for bottom culture largely serving the cannery market, and container culture largely serving the half-shell market, to reflect different production costs, market prices, and oyster growth and survival. The coupled hydrodynamic-biogeochemical and oyster ecology models projected high spatial variability in oyster growth and mortality with the most favorable growing conditions in the lower north and upper mid bay, where mortality is lowest, and the upper south bay, where growth is highest. Climate change by late mid-century generated modest water quality changes and virtually no mortality rate changes. Nonetheless, our modeling revealed that even if growers made optimal management choices under uncertainty, the majority of modeled sites would see a decline in profitability under climate change, primarily due to potential reductions in food availability. Bottom culture was more resilient to the future climate at most sites, being less sensitive to small changes in growth than container culture. Information on how aquaculture conditions currently vary in space was more important for profitability than future climate forecasts. Our stochastic dynamic programming approach tailored grower behavior to each site and unfolding annual conditions, including highly targeted and cost-effective mitigation adjustments to boost oyster survival or growth., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The annual compliance cycle of the carbon trading system gives generation companies (GenCos) the flexibility to buy allowances at preferred times rather than the same time that emissions happen. How to estimate the actual emission cost under fluctuating carbon prices and make better generation decisions become challenges for GenCos. A synergistic generation and carbon trading optimization method is proposed in this paper with a non-speculative dynamic (NSD) carbon trading strategy. The NSD strategy utilizes stochastic dynamic programming to make optimal carbon trading decisions and to provide accurate emission cost estimations for GenCos to make generation decisions. To control risks, the NSD strategy forbids intentional speculations in the carbon market but allows both buying and selling of allowances according to the yearly total emissions estimated by the generation strategy. A risk control scheme by adjusting the allowance-emission balancing period is proposed so that GenCos can smoothly shift between the traditional daily-balanced carbon trading strategy and the NSD strategy. Properties of the NSD strategy are analyzed and proved theoretically under some sufficient conditions, which makes the strategy interpretable and trustworthy. Numerical simulations verify that the proposed method can help GenCos lower their emission costs and improve their overall profits in a year. • The annual compliance cycle of the carbon trading system is considered in the synergistic daily generation and carbon trading decisions of generation companies. • Stochastic dynamic programming is used to optimize daily carbon trading transactions and estimate the actual emission cost in a year. • Risks are controlled by setting the allowance-emission balancing periods. [ABSTRACT FROM AUTHOR]

Container slot allocation represents a critical operational decision-making challenge within the liner shipping industry, which necessitates making decisions on the transportation of loaded containers and the repositioning of empty ones under stochastic demand. We study novel dynamic allocation policy that leverage sequentially-revealed demand information to determine the slot allocation decision for both loaded and empty containers at each stage. In this paper, we develop a stochastic dynamic programming (DP) model to optimize the slot allocation decision for maximizing the expected total revenue over the planning horizon. To solve this model, we design an efficient allocation policy that makes slot allocations at each stage based on current empty container stocks, realized demand, and the mean demand at future stages. Comprehensive numerical experiments on both synthetic and realistic data demonstrate substantial revenue improvement of our approach over the commonly-used benchmark policies in practice and literature. • We develop a stochastic dynamic program for container slot allocation with empty container repositioning under stochastic demand. • We design a dynamic allocation policy that sequentially determines the slot allocation decision based on the realized information at each stage. • A series of numerical experiments are carried out to show that our approach can outperform the benchmark policies used in practice and literature. [ABSTRACT FROM AUTHOR]

The use of chemical weapons has turned into an increasing risk for the world. In this study, a hybrid approach on the basis of fuzzy VIKOR and the optimal search model to cope with an important case study called the detection of illegal chemical warehouses is introduced. It is obvious that such illegal activities are accomplished under high secret considerations. Therefore, we have several types of ambiguity and uncertainty in this problem. First, fuzzy VIKOR is used to prioritize the suspicious warehouses based on time and cost of a search under fuzzy environment. Also the probability of existence of chemical agents in each warehouse (P i) and the probability of detection (α i) in case materials exist in warehouse, are estimated. Next, the output of VIKOR i.e., Q is assumed as an input of optimal search model and optimal strategy for searching is achieved by solving a stochastic dynamic programming. According to this hybrid approach, we start from the location that has the maximum value of α i P i Q i . Although we get benefits of fuzzy logic, VIKOR, and classical optimal search, the suggested method is easy to understand and straightforward to utilize in real-world problems. Also this model can enhance the robust nature of hybrid approach and reduces its sensitivity to the change of weights. [ABSTRACT FROM AUTHOR]

A swarm of unmanned aerial vehicles (UAVs) requires the transmission of mission-related data across the network. The resource constraints and dynamic nature of the swarm bring critical challenges to the design of UAV routing protocols. Most of the conventional ad hoc routing schemes are not intelligent and cannot adapt to the dynamic nature of UAV swarming networks. On the other hand, some artificial intelligence (AI)-based routing schemes may consume significant computational resources in the UAVs. In this article, a low-cost, adaptive routing protocol, namely skeleton-based swarm routing (SSR), is proposed, which exploits an intelligent online learning algorithm and the topology features of the mission-driven UAV swarm to distribute the traffic over optimal routes. Here, the skeleton represents the most stable parts of the swarm formation. SSR architecture consists of three modules: 1) A geometric addressing module, which assigns geometric coordinates to each node based on the swarm skeleton structure; 2) A leaf-like routing pipe which allows the selection of multiple candidate routes around the shortest path; 3) An intelligent low-complexity learning model which determines how to distribute the packets inside the routing pipe to achieve load-balanced, high-throughput transmissions. The proposed skeleton-based scheme can also facilitate the UAV formation construction and morphing. The simulation results show that the proposed SSR protocol can noticeably improve the network performance (up to 100% throughput improvement) compared to the single path routing schemes, such as the ad-hoc on-demand distance vector (AODV) and link-quality and traffic-load aware optimized link state routing (LTA-OLSR) protocols.

With the rapid development of economy and technology, large-scale integrated energy buildings account for an increasing proportion of urban load. However, the randomness of EV owner behaviors, electricity price and outdoor temperature have brought challenges to the energy management of integrated energy buildings. This paper proposes a stochastic dynamic programming-based online algorithm to address the energy management of integrated energy buildings with electric vehicles and flexible thermal loads under multivariate uncertainties. First, an online energy management framework is established, which is further formulated as a multi-stage stochastic sequential decision-making problem. To address the complexities of the problem, a novel stochastic dynamic programming is employed to develop a distribution-free, computationally efficient, and scalable solution. By using extensive training samples, the algorithm is trained offline to learn how to deal with multivariate uncertainties and get the approximate optimal solution, which no longer depends on intraday forecast information. Numerical tests demonstrate the effectiveness of the proposed algorithm compared with other online algorithms in terms of optimality and computation efficiency.

Iteroparous parents face a trade-off between allocating current resources to reproduction versus maximizing survival to produce further offspring. Parental allocation varies across age and follows a hump-shaped pattern across diverse taxa, including mammals, birds and invertebrates. This nonlinear allocation pattern lacks a general theoretical explanation, potentially because most studies focus on offspring number rather than quality and do not incorporate uncertainty or age-dependence in energy intake or costs. Here, we develop a life-history model of maternal allocation in iteroparous animals. We identify the optimal allocation strategy in response to stochasticity when energetic costs, feeding success, energy intake and environmentally driven mortality risk are age-dependent. As a case study, we use tsetse, a viviparous insect that produces one offspring per reproductive attempt and relies on an uncertain food supply of vertebrate blood. Diverse scenarios generate a humpshaped allocation when energetic costs and energy intake increase with age and also when energy intake decreases and energetic costs increase or decrease. Feeding success and environmentally driven mortality risk have little influence on age-dependence in allocation. We conclude that ubiquitous evidence for age-dependence in these influential traits can explain the prevalence of nonlinear maternal allocation across diverse taxonomic groups. [ABSTRACT FROM AUTHOR]

The literature on green mobility and eco-driving in urban areas has burgeoned in recent years, with special attention to using infrastructure to vehicle (I2V) communications to obtain optimal speed trajectory which minimize the economic and environmental costs. This article shares the concept with these studies but turns the spotlight on cyclists. It examines the problem of finding optimal speed trajectory for a cyclist in signalised urban areas. Unlike the available studies on motorised vehicles which predominantly designed for pre-defined, fixed traffic lights timing, this article uses an algorithm based on stochastic dynamic programming to explicitly address uncertainty in traffic light timing. Moreover, through a comprehensive set of simulation experiments, the article examines the impact of the speed advice’s starting point as well as the cyclist’s willingness for changing his/her speed on enhancing the performance. The proposed approach targets various performance metrics such as minimising the total travel time, energy consumption, or the probability of stopping at a red light. Hence, the resulting speed advice can be tailored according to the personal preferences of each cyclist. In a simulation case study, the results of the proposed approach is also compared with an existing approach in the literature. [ABSTRACT FROM AUTHOR]

Coordination of networked microgrids (MGs) offers a promising solution to utilize distributed resources flexibilities and accommodate renewable energy. This paper studies real-time coordination of distribution system operation (DSO) and MGs considering multivariate uncertainty. Current researches suffer from inadaptability to dynamic system uncertainty, extensive iterations, and dependence on prediction. To fill these gaps, A novel multi-agent learning based stochastic dynamic programming (MASDP) is proposed to obtain the optimal policy for entities. Specifically, transactive energy control (TEC) mechanism, which requires only market-based information interactions, is employed to facilitate coordination between MG and DSO. A data-driven offline self-learning is proposed for entities to learn how to manage resources in response to system uncertainty. After sufficient offline learning, online operation of MASDP can be run in both non-iterative and iterative manners, by which near-optimal/optimal real-time solutions of DSO and MGs can be given sequentially and distributedly. Numerical comparisons with state-of-art policies and TEC algorithms verify the optimality, efficiency, adaptability, and scalability of MASDP. [ABSTRACT FROM AUTHOR]

In this paper, we study a Markov decision process with a non-linear discount function and with a Borel state space. We define a recursive discounted utility, which resembles non-additive utility functions considered in a number of models in economics. Non-additivity here follows from non-linearity of the discount function. Our study is complementary to the work of Jaśkiewicz et al. (Math Oper Res 38:108–121, 2013), where also non-linear discounting is used in the stochastic setting, but the expectation of utilities aggregated on the space of all histories of the process is applied leading to a non-stationary dynamic programming model. Our aim is to prove that in the recursive discounted utility case the Bellman equation has a solution and there exists an optimal stationary policy for the problem in the infinite time horizon. Our approach includes two cases: (a) when the one-stage utility is bounded on both sides by a weight function multiplied by some positive and negative constants, and (b) when the one-stage utility is unbounded from below. [ABSTRACT FROM AUTHOR]

Acoustic disturbance is a growing conservation concern for wildlife populations because it can elicit physiological and behavioral responses that can have cascading impacts on population dynamics. State‐dependent behavioral and life history models implemented via Stochastic Dynamic Programming (SDP) provide a natural framework for quantifying biologically meaningful population changes resulting from disturbance by linking environment, physiology, and metrics of fitness. We developed an SDP model using the endangered western gray whale (Eschrichtius robustus) as a case study because they experience acoustic disturbance on their summer foraging grounds. We modeled the behavior and physiological dynamics of pregnant females as they arrived on the feeding grounds and predicted the probability of female and offspring survival, with and without acoustic disturbance and in the presence/absence of high prey availability. Upon arrival in mid‐May, pregnant females initially exhibited relatively random behavior before they transitioned to intensive feeding that resulted in continual fat mass gain until departure. This shift in behavior co‐occurred with a change in spatial distribution; early in the season, whales were more equally distributed among foraging areas with moderate to high energy availability, whereas by mid‐July whales transitioned to predominate use of the location that had the highest energy availability. Exclusion from energy‐rich offshore areas led to reproductive failure and in extreme cases, mortality of adult females that had lasting impacts on population dynamics. Simulated disturbances in nearshore foraging areas had little to no impact on female survival or reproductive success at the population level. At the individual level, the impact of disturbance was unequally distributed across females of different lengths, both with respect to the number of times an individual was disturbed and the impact of disturbance on vital rates. Our results highlight the susceptibility of large capital breeders to reductions in prey availability, and indicate that who, where, and when individuals are disturbed are likely to be important considerations when assessing the impacts of acoustic activities. This model provides a framework to inform planned acoustic disturbances and assess the effectiveness of mitigation strategies for large capital breeders. [ABSTRACT FROM AUTHOR]

In this paper, a single-product, single-machine system under Markovian deterioration of machine condition and demand uncertainty is studied. The objective is to find the optimal intervals for inspection and preventive maintenance (PM) activities in a condition-based maintenance planning with discrete monitoring (CBMDM) framework. At first, a stochastic dynamic programming model whose state variable is the machine status is presented. This model whose objective is minimizing inspection, preventive maintenance and lost production costs due to the difference in actual capacity with nominal capacity, does not take into account demand. Then, demand is appended to the state variable in the second model and the average cost of lost production which is due to the difference in actual production capacity with demand is replaced in the first model correspondingly. Finally, to validate and analyze the proposed models, an application of the models in wind turbines is prepared. The numerical results show that replacing demand by nominal capacity in simultaneous inspection and preventive maintenance planning, the average total costs consist of inspection and preventive maintenance in the planning horizon are reduced in the planning horizon.

Abstract 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 significance of behavioral unpredictability.

Personnel retention is one of the most significant challenges faced by the US Army. Central to the problem is understanding the incentives of the stay-or-leave decision for military personnel. Using three years of data from the US Department of Defense, we construct and estimate a Markov chain model of military personnel. Unlike traditional classification approaches, such as logistic regression models, the Markov chain model allows us to describe military personnel dynamics over time and answer a number of managerially relevant questions. Building on the Markov chain model, we construct a finite-horizon stochastic dynamic programming model to study the monetary incentives of stay-or-leave decisions. The dynamic programming model computes the expected pay-off of staying versus leaving at different stages of the career of military personnel, depending on employment opportunities in the civilian sector. We show that the stay-or-leave decisions from the dynamic programming model possess surprisingly strong predictive power, without requiring personal characteristics that are typically employed in classification approaches. Furthermore, the results of the dynamic programming model can be used as an input in classification methods and lead to more accurate predictions. Overall, our work presents an interesting alternative to classification methods and paves the way for further investigations on personnel retention incentives. [ABSTRACT FROM PUBLISHER]

One of the most important and effective works of water resource planning and management is determining the specific, applicable, regulated operating policies of the Zayandehroud dam reservoir, as a case study, in which it should be user-friendly and straightforward for the operator. For this purpose, different methods have been proposed in which each of them has its limitations. Due to the unique capabilities of the genetic programming (GP) model, here, this method is used to determine the operating rule curves and policies of the dam reservoir. For this purpose, here, two cases are proposed in which, in the first case, each month is individually simulated and modeled. However, in the second case, all months are simulated simultaneously. A second case is proposed here to determine simple and more applicable operation rule curves. In addition, two approaches are suggested for each case in which in the first approach, the influential input variables are selected by presenting the hybrid method. In the proposed hybrid method, the artificial neural network (ANN) model is equipped with non-dominated sorting genetic (NSGA-II) algorithm leading to a hybrid method named the ANN-NSGA-II method. However, in the second approach, the influential input variables are selected automatically using the GP method. Here, the hybrid method is proposed and used to overcome the limitations of existing usual method. In other words, it is proposed to reduce the number of influential input variables of data-driven methods and select the effective ones. The obtained results of all proposed cases and approaches are presented and compared with the standard operation policy method, stochastic dynamic programming, ANN model, and NLP method. Comparison of the results shows the acceptable performance of the proposed cases and approaches. In other words, the best-obtained values of (stability index) SI index and water deficit (objective function value) are 49.3% and 32, respectively. [ABSTRACT FROM AUTHOR]

Partially observable Markov decision processes (POMDPs) are a convenient mathematical model to solve sequential decision‐making problems under imperfect observations. Most notably for ecologists, POMDPs have helped solve the trade‐offs between investing in management or surveillance and, more recently, to optimise adaptive management problems.Despite an increasing number of applications in ecology and natural resources, POMDPs are still poorly understood. The complexity of the mathematics, the inaccessibility of POMDP solvers developed by the Artificial Intelligence (AI) community, and the lack of introductory material are likely reasons for this.We propose to bridge this gap by providing a primer on POMDPs, a typology of case studies drawn from the literature, and a repository of POMDP problems.We explain the steps required to define a POMDP when the state of the system is imperfectly detected (state uncertainty) and when the dynamics of the system are unknown (model uncertainty). We provide input files and solutions to a selected number of problems, reflect on lessons learned applying these models over the last 10 years and discuss future research required on interpretable AI.Partially observable Markov decision processes are powerful decision models that allow users to make decisions under imperfect observations over time. This primer will provide a much‐needed entry point to ecologists. [ABSTRACT FROM AUTHOR]

Consumption-saving models with adjustment costs or discrete choices are typically hard to solve numerically due to the presence of non-convexities. This paper provides a number of tools to speed up the solution of such models. Firstly, I use that many consumption models have a nesting structure implying that the continuation value can be efficiently pre-computed and the consumption choice solved separately before the remaining choices. Secondly, I use that an endogenous grid method extended with an upper envelope step can be used to solve efficiently for the consumption choice. Thirdly, I use that the required pre-computations can be optimized by a novel loop reordering when interpolating the next-period value function. As an illustrative example, I solve a model with non-durable consumption and durable consumption subject to adjustment costs. Combining the provided tools, the model is solved almost 50 times faster than with standard value function iteration for a given level of accuracy. Software is provided in both Python and C++. [ABSTRACT FROM AUTHOR]

This paper presents a comparative study of two promising real-time energy management strategies for fuel cell electric vehicle applications: adaptive equivalent consumption minimization strategy (A-ECMS), and stochastic dynamic programming (SDP). An off-line algorithm –classic dynamic programming - provides reference results. On-line and off-line strategies are tested both in simulation and using a dedicated test bench completely consistent with an electric scooter powertrain. The hybrid power source combines a fuel cell, a supercapacitor pack and two related power converters. The system model is carefully calibrated using experimental data. This allows meaningful identification of parameters of the various strategies. The model data is determined using a motorcycle certification driving cycle. The robustness of each strategy is then analyzed using a large number of random driving cycles. Experimental and simulation results show that a specific SDP approach, based on Markov chain modeling, has the best overall performance in real-world driving conditions. It achieves the minimum average hydrogen consumption while respecting the state-sustaining constraint. Conversely, the A-ECMS results lack robustness and show poor performance indexes when facing unknown real world power demand profile. In conclusion, the present results indicate SDP is an interesting approach for future hybrid source energy allocation. • Novel statistical assessment of pure on-line EMS regarding real–life use. • A Markov chain model is used to generate random and realistic power profiles. • Pure DP provides an absolute reference and EMS are assessed with 2 clear performance indexes. • Experiments show SDP as a promising EMS. [ABSTRACT FROM AUTHOR]

Combined simulation–optimization (CSO) schemes are common in the literature to solve different groundwater management problems, and CSO is particularly well-established in the coastal aquifer management literature. However, with a few exceptions, nearly all previous studies have employed the CSO approach to derive static groundwater management plans that remain unchanged during the entire management period, consequently overlooking the possible positive impacts of dynamic strategies. Dynamic strategies involve division of the planning time interval into several subintervals or periods, and adoption of revised decisions during each period based on the most recent knowledge of the groundwater system and its associated uncertainties. Problem structuring and computational challenges seem to be the main factors preventing the widespread implementation of dynamic strategies in groundwater applications. The objective of this study is to address these challenges by introducing a novel probabilistic Multiperiod CSO approach for dynamic groundwater management. This includes reformulation of the groundwater management problem so that it can be adapted to the multiperiod CSO approach, and subsequent employment of polynomial chaos expansion-based stochastic dynamic programming to obtain optimal dynamic strategies. The proposed approach is employed to provide sustainable solutions for a coastal aquifer storage and recovery facility in Oman, considering the effect of natural recharge uncertainty. It is revealed that the proposed dynamic approach results in an improved performance by taking advantage of system variations, allowing for increased groundwater abstraction, injection and hence monetary benefit compared to the commonly used static optimization approach. [ABSTRACT FROM AUTHOR]