This paper investigates how the uncertainty of future expected values influences decision making among non-industrial private woodlot owners. A model of a red spruce plantation in the context of New Brunswick, Canada,...
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This paper investigates how the uncertainty of future expected values influences decision making among non-industrial private woodlot owners. A model of a red spruce plantation in the context of New Brunswick, Canada, is constructed using stochastic dynamic programming, where risks from natural disasters and market fluctuations are modeled as a Markov process. It is used to investigate whether an increase in perceived risk can justify harvesting plantations much earlier than planned. Findings indicate that no plausible natural disaster scenarios can warrant harvesting substantially earlier than optimal under a risk-free scenario for a risk neutral decision maker. However, the observed sensitivity to the discount rate suggests that early harvesting may reflect the behaviour of a risk-averse landowner confronting increased perceived risk. Such behaviour may result in suboptimal value from the viewpoint of plantation subsidy program managers. These results highlight the importance of reassessing subsidy programs to find the right balance between societal objectives and those of non-industrial private woodlot owners.
This article presents a hybrid approach to enhance the path planning for unmanned ground vehicles (UGVs) by combining stochastic dynamic programming (SDP) based hybrid model predictive control (HMPC) with Dijkstra-bas...
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This article presents a hybrid approach to enhance the path planning for unmanned ground vehicles (UGVs) by combining stochastic dynamic programming (SDP) based hybrid model predictive control (HMPC) with Dijkstra-based pseudo priority queues (PPQ). The proposed approach employs a platform-specific model that takes into account skid-slip effects, a global cost-to-go (CTG) function, and dynamic obstacles. To achieve efficient path planning on large maps, the proposed approach employs a Dijkstra algorithm and PPQ to generate the CTG function. Moreover, the HMPC process incorporates the latest CTG information, the vehicle model, and the perceived environment, encompassing both static and dynamic obstacles. Extensive simulations have been conducted to comprehensively evaluate the performance of the proposed technique. Through a comparative analysis against existing path planners, the results demonstrate the effectiveness and superiority of the hybrid approach.
This paper proposes a deep learning approach for solving optimal stopping problems and high-dimensional American-style options pricing problems. Through state-space partition, the method does not require recalculation...
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This paper proposes a deep learning approach for solving optimal stopping problems and high-dimensional American-style options pricing problems. Through state-space partition, the method does not require recalculation of the structure of networks when the price of the asset changes, which makes tracking valuation more efficient. This paper also offers theoretical proof for the existence of a deep learning network that can determine the optimal stopping time via state-space partition. We present convergence proofs for the estimators and also test the method on Bermuda max-call options as examples.
During an investigation using Forensic Investigative Genetic Genealogy, which is a novel approach for solving violent crimes and identifying human remains, reference testing-when law enforcement requests a DNA sample ...
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During an investigation using Forensic Investigative Genetic Genealogy, which is a novel approach for solving violent crimes and identifying human remains, reference testing-when law enforcement requests a DNA sample from a person in a partially constructed family tree-is sometimes used when an investigation has stalled. Because the people considered for a reference test have not opted in to allow law enforcement to use their DNA profile in this way, reference testing is viewed by many as an invasion of privacy and by some as unethical. We generalize an existing mathematical optimization model of the genealogy process by incorporating the option of reference testing. Using simulated versions of 17 DNA Doe Project cases, we find that reference testing can solve cases more quickly (although many reference tests are required to substantially hasten the investigative process), but only rarely (<1%) solves cases that cannot otherwise be solved. Through a mixture of mathematical and computational analysis, we find that the most desirable people to test are at the bottom of a path descending from an ancestral couple that is most likely to be related to the target. We also characterize the rare cases where reference testing is necessary for solving the case: when there is only one descending path from an ancestral couple, which precludes the possibility of identifying an intersection (e.g., marriage) between two descendants of two different ancestral couples.
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 reposit...
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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.
Our framework deals with stochasticdynamic inventory models for stocking decisions of a retailer selling a single perishable product in the presence of strategic customers who time their purchases. Each short period,...
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Our framework deals with stochasticdynamic inventory models for stocking decisions of a retailer selling a single perishable product in the presence of strategic customers who time their purchases. Each short period, the retailer determines a stocking quantity before random demand is realized. Strategic customers use their reference on product availability to purchase at a regular price or wait for a markdown and learn from the retailer's stocking quantity to update their reference. We characterize the structural properties such as the concavity of single- and two-period profit functions. On an infinite horizon, we show that a steady-state reference distribution is ergodic and an optimal stocking quantity is unique for a given reference. We conduct extensive numerical studies on an infinite horizon to compare an optimal dynamic policy and the corresponding optimal static policy which sets a fixed stocking quantity over time. A near-optimal performance of optimal static policy with an average profit gap of less than 1% is remarkable and contrasts with that in the two-period model which may be far worse. Thus, a well-chosen fixed stocking quantity on a planning horizon with many short periods tends to yield a high performance without having to change stocking quantities over time.
We are concerned about a multi-period portfolio selection problem where the issue of parameter uncertainty for the distribution of risky asset returns should be addressed properly. For analysis, we first propose a nov...
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We are concerned about a multi-period portfolio selection problem where the issue of parameter uncertainty for the distribution of risky asset returns should be addressed properly. For analysis, we first propose a novel dynamic portfolio selection model with an l(infinity) risk function, instead of the classic portfolio variance, used as risk measure. The investor in our model is assumed to choose the optimal portfolio by maximizing the expected terminal wealth at a minimum level of cumulative risk, quantified by a weighted sum of the risks in subsequent periods. The proposed multi-period model has a closed-form optimal policy that can be constructed and interpreted intuitively. We introduce Bayesian learning to account for the uncertainty in estimates of unknown parameters and discuss the impact of Bayesian learning on the investor's decision making. Under an i.i.d. normal return-generating process with unknown means and covariance matrix, we show how Bayesian learning promotes diversification and reduces sensitivity of optimal portfolios to changes in model inputs. The numerical results based on real market data further support that the model with Bayesian learning can perform much better than a plug-in model out-of-sample with the extent of performance improvement affected by the investor's level of risk aversion and the amount of data available.
Collective animal behaviour is a subfield of behavioural ecology, making extensive use of its tools of observation, experimental manipulation and model building. However, a fundamental behavioural ecology approach, th...
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Collective animal behaviour is a subfield of behavioural ecology, making extensive use of its tools of observation, experimental manipulation and model building. However, a fundamental behavioural ecology approach, the application of optimality theory, has been comparatively neglected in collective animal behaviour. This article seeks to address this imbalance, by outlining an evolutionary theory framework for the discipline. The application of optimality theory to collective animal behaviour requires a number of questions to be addressed. First, what is the correct quantity to optimize? This can be achieved via a combination of considering the organisms' life history, alongside tools such as statistical decision theory and stochastic dynamic programming. Second, what mechanism is appropriate for optimal behaviour? This involves ensuring that models are self-consistent rather than assuming parameter values. Third, at what level of selection does optimization act? Selection acts on the individual except in very particular circumstances, yet collective animal behaviour phenomena are group level, thus introducing a risk of confusing at what level adaptive properties emerge. This article presents examples under each of the three questions, as well as discussing mismatches between theory and observation. In doing so, it is hoped that collective animal behaviour fully inherits the tools and philosophy of its parent discipline of behavioural ecology. (c) 2024 The Author(s). Published by Elsevier Ltd on behalf of The Association for the Study of Animal Behaviour. This is an open access article under the CC BY license (http://***/licenses/ by/4.0/).
We deal with the problem of managing a project or a complex operational process by controlling the execution pace of the activities it comprises. We consider a setting in which these activities are clearly defined, ar...
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We deal with the problem of managing a project or a complex operational process by controlling the execution pace of the activities it comprises. We consider a setting in which these activities are clearly defined, are subject to precedence constraints, and progress randomly. We formulate a discrete-time, infinite-horizon Markov decision process in which the manager reviews progress in each period and decides which activities to expedite to balance expediting costs with delay costs. We derive structural properties for this dynamic project expediting problem. These enable us then to devise exact solution methods that we show to reduce computational burden significantly. We illustrate how our method generalizes and can be used to tackle a wide range of so-called stochastic shortestpath problems that are characterized by an intuitive property and can capture other applications, including medical decision-making and disease-modeling problems. Moreover, we also deal with the state identification issue for our problem, which is a challenging task in and of itself, owing to precedence constraints. We complement our analytical results with numerical experiments, demonstrating that both our solution and state identification methods significantly outperform extant methods for a supply chain example and for various randomly generated instances.
Oversaturation during peak hours brings about severe challenges for metro operations management in megacities. It can deteriorate passengers' service level experience and increase the safety risk at congested plat...
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Oversaturation during peak hours brings about severe challenges for metro operations management in megacities. It can deteriorate passengers' service level experience and increase the safety risk at congested platforms. This paper focuses on designing an online passenger flow control policy to manage passenger flow in each origin-destination (OD) pair so that the total passenger waiting time during the research horizon can be minimized. Suppose that the OD demand information reveals sequentially over time, we formulate the online passenger flow control problem as stochastic dynamic programming (DP). An efficient online adaptive policy is designed to guide the real-time flow control decisions at each stage. To evaluate the performance of our approach, we exploit the realistic transit data from the Beijing metro system to carry out a series of numerical experiments. The computational results show that our approach can significantly reduce the expected total passenger waiting time as well as alleviate metro station congestion compared with the first-come-first-serve (FCFS) policy. The benefits of our approach are obtained by exploiting the reusable nature of train capacity to transport more passengers during rush hours.(c) 2023 Elsevier Ltd. All rights reserved.
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