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An evolutionary model of sensitive periods when the reliability of cues varies across ontogeny

BEHAVIORAL ECOLOGY 2022年第1期33卷 101-114页

作者： Walasek, Nicole Frankenhuis, Willem E. Panchanathan, Karthik Radboud Univ Nijmegen Behav Sci Inst Thomas van Aquinostr 4 NL-6525 GD Nijmegen Netherlands Univ Utrecht Dept Psychol Heidelberglaan 1 NL-3584 CS Utrecht Netherlands Max Planck Inst Study Crime Secur & Law Gunterstalstr 73 D-79100 Freiburg Germany Univ Missouri Dept Anthropol 225 Swallow Hall Columbia MO 65211 USA

Sensitive periods are widespread in nature, but their evolution is not well understood. Recent mathematical modeling has illuminated the conditions favoring the evolution of sensitive periods early in ontogeny. However, sensitive periods also exist at later stages of ontogeny, such as adolescence. Here, we present a mathematical model that explores the conditions that favor sensitive periods at later developmental stages. In our model, organisms use environmental cues to incrementally construct a phenotype that matches their environment. Unlike in previous models, the reliability of cues varies across ontogeny. We use stochastic dynamic programming to compute optimal policies for a range of evolutionary ecologies and then simulate developmental trajectories to obtain mature phenotypes. We measure changes in plasticity across ontogeny using study paradigms inspired by empirical research: adoption and cross-fostering. Our results show that sensitive periods only evolve later in ontogeny if the reliability of cues increases across ontogeny. The onset, duration, and offset of sensitive periods-and the magnitude of plasticity-depend on the specific parameter settings. If the reliability of cues decreases across ontogeny, sensitive periods are favored only early in ontogeny. These results are robust across different paradigms suggesting that empirical findings might be comparable despite different experimental designs. Phenotypic plasticity, the ability to tailor development to different conditions, typically declines with age. However, some traits have sensitive periods, windows of heightened plasticity, at later life stages, such as adolescence. The evolution of such windows is not well understood. Using mathematical modeling we find that changes in the reliability of information available to developing organisms may influence when sensitive periods evolve. Specifically, when the reliability of information increases across ontogeny, sensitive periods evolve later.

关键词： cues development evolution modeling phenotypic plasticity sensitive periods stochastic dynamic programming

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Managing the impact of invasive species: the value of knowing the density-impact curve

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ECOLOGICAL APPLICATIONS 2009年第2期19卷 376-386页

作者： Yokomizo, Hiroyuki Possingham, Hugh P. Thomas, Matthew B. Buckley, Yvonne M. CSIRO Sustainable Ecosyst St Lucia Qld 4067 Australia Univ Queensland Sch Integrat Biol Ctr Ecol Brisbane Qld 4072 Australia Penn State Univ Ctr Infect Dis Dynam University Pk PA 16802 USA Penn State Univ Dept Entomol Chem Ecol Lab University Pk PA 16802 USA

Economic impacts of invasive species worldwide are substantial. Management strategies have been incorporated in population models to assess the effectiveness of management for reducing density, with the implicit assumption that economic impact of the invasive species will also decline. The optimal management effort, however, is that which minimizes the sum of both the management and impact costs. The relationship between population density and economic impact (what we call the "density-impact curve'') is rarely examined in a management context and could take several nonlinear forms. Here we determine the effects of population dynamics and density-impact curves of different shapes on optimal management effort and discover cases where management is either highly effective or a waste of resources. When an inaccurate density-impact curve is used, the increase in total costs due to over- or underinvestment in management can be large. We calculate the increase in total costs incurred if the density-impact curve is incorrect and find that the greater the maximum impact caused by an invasive species, the more important it is not only to reduce its density, but also to know the shape of the density-impact relationship accurately. Lack of information regarding the relationship between density and economic impact causes the most acute problems for invaders that cause high impact at low density, where management typically will be too little, too late. For species that are only problematic at high density, ignorance of the density-impact curve can lead to overinvestment in management with little reduction in impact.

关键词： cost of impact density dependence invasive species modeling economic impact of pests and their control stochastic dynamic programming value of information weed management

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Optimal specialty crop planning policies with yield learning and forward contract

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PRODUCTION AND OPERATIONS MANAGEMENT 2023年第2期32卷 359-378页

作者： Chen, Heng Ryan, Jennifer K. Univ Nebraska Coll Business Dept Supply Chain Management & Analyt Lincoln NE 68588 USA

Hops are the flowers of a specialty crop that provide unique flavors to craft beer. We study a multi-year crop planning problem for a farmer who seeks to add hops production to the current production of a conventional crop. The farmer must dynamically determine the number of acres to allocate to each crop and design the terms of the forward contract under which brewers purchase the hops, considering the uncertainty of weather conditions, hops yield, hops spot market price, and conventional crop price. We formulate a multi-period stochastic dynamic programming framework that incorporates statistical learning methods that depend on exogenous factors. We also develop an easy-to-implement learning-based marginal total profit heuristic which can potentially be used as a decision support tool. Our numerical analyses suggest that yield learning is particularly important for a farmer who is considering investing in a high margin, but potentially risky, new crop such as hops. We also characterize the conditions under which yield learning is most beneficial for farmers. This paper contributes to the literature on specialty crop planning by introducing a multi-year planning framework that incorporates unique characteristics of specialty crop production. We fill a gap in the crop planning literature by considering how farmers can learn about crop yields based on realized yields and exogenous factors such as weather conditions. Our paper is also of practical importance for farmers who seek to diversify their crop portfolio to hedge against risks associated with trade tensions and potential price drops for conventional crops.

关键词： agricultural operations management forward contract specialty crop planning stochastic dynamic programming yield learning

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A stochastic Control Approach to Optimally Designing Hierarchical Flash Sets in P300 Communication Prostheses

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2012年第1期20卷 102-112页

作者： Ma, Rui Aghasadeghi, Navid Jarzebowski, Julian Bretl, Timothy Coleman, Todd P. Univ Calif San Diego Dept Bioengn La Jolla CA 92093 USA Univ Calif San Diego Inst Neural Computat La Jolla CA 92093 USA Univ Illinois Dept Elect & Comp Engn Urbana IL 61801 USA Univ Illinois Dept Aerosp Engn Urbana IL 61801 USA Univ Illinois Neurosci Program Urbana IL 61801 USA Motorola Inc Schaumburg IL 60196 USA

The P300-based speller is a well-established brain-computer interface for communication. It displays a matrix of objects on the computer screen, flashes each object in sequence, and looks for a P300 response induced by flashing the desired object. Most existing P300 spellers uses a fixed set of flash objects. We demonstrate that performance can be significantly improved by sequential selections from a hierarchy of flash sets containing variable number of objects. Theoretically, the optimal hierarchy of flash sets-with respect to a given statistical language model-can be found by solving a stochastic control problem of low computational complexity. Experimentally, statistical analysis demonstrates that the average time per output character at 85% accuracy is reduced by over 50% using our variable-flash-set approach as compared to traditional fixed-flash-set spellers.

关键词： Brain-computer interfaces (BCIs) hierarchical flash sets stochastic dynamic programming P300

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Expected Future Value Decomposition Based Bid Price Generation for Large-Scale Network Revenue Management

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TRANSPORTATION SCIENCE 2013年第2期47卷 181-197页

作者： Escudero, L. F. Monge, J. F. Morales, D. Romero Wang, J. Univ Rey Juan Carlos Dept Estadist & Invest Operativa Madrid Spain Univ Miguel Hernandez Ctr Invest Operativa Elche Alicante Spain Univ Oxford Said Business Sch Oxford England

This paper studies a multistage stochastic programming (SP) model for large-scale network revenue management. We solve the model by means of the so-called expected future value (EFV) decomposition via scenario analysis, estimating the impact of the decisions made at a given stage on the objective function value related to the future stages. The EFV curves are used to define bid prices on bundles of resources directly, as opposed to the traditional additive approach. We compare our revenues to those obtained by additive bid prices, such as the bid prices derived from the deterministic equivalent model (DEM) of the compact representation of the SP model. Our computational experience shows that the revenues obtained by our approach are better for middle-range values of the load factor of demand, whereas the differences among all the approaches we have tested are insignificant for extreme values. Moreover, our approach requires significantly less computation time than does the optimization of DEM by plain use of optimization engines. Problem instances with 72 pairs of bundle-fare classes have been solved in less than one minute, with 800 pairs in less than five minutes, and with 4,000 pairs in less than one hour. The time taken by DEM was, in general, of one order of magnitude higher. Finally, for the three largest problem instances, and after two hours, the expected revenue returned by DEM was below that obtained by EFV by 13.47%, 17.14%, and 38.94%, respectively.

关键词： network revenue management scenario trees stochastic dynamic programming expected future value curves nonadditive bid prices load factor of demand

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Model Predictive Control With stochastically Approximated Cost-to-Go for Battery Cooling System of Electric Vehicles

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2021年第5期70卷 4312-4323页

作者： Park, Seho Ahn, Changsun Pusan Natl Univ Busan 46241 South Korea Penn State Univ University Pk PA 16802 USA

The battery thermal management system of an electric vehicle consumes considerable energy when cooling the battery, which can reduce the driving range. To minimize the energy consumption of the battery cooling system, controllers need to be designed as an optimal control problem. A model predictive control can be applied to the optimal controller design, which can be implemented in real-time but at the cost of a small loss of optimality. The performance of a model predictive controller is affected by its cost structure, which is typically composed of the transition cost and the terminal cost. The transition cost is defined by the controller objective, and energy consumption is one example. However, the terminal cost is user defined and it is the main design factor for the controller performance. In model predictive control, the terminal cost is usually formulated to penalize the state variations, which can cause loss of optimality. In this study, the terminal cost is formulated to represent the cost from the end of the prediction horizon to infinity, which is called the cost-to-go. This approach is consistent at the point of an optimal control problem, and the controller with cost-to-go can achieve more optimal performance than one that penalizes state variations. In the proposed model predictive controller, the cost-to-go is approximated by the optimal expected cost that can be calculated using stochastic dynamic programming. The proposed controller reduces the energy consumption significantly in comparison to a typical model predictive controller without increasing the computing load.

关键词： Batteries Cooling Thermal management Refrigerants Heating systems Energy consumption Electric vehicles Battery Thermal Management Model Predictive Control dynamic programming stochastic dynamic programming Electric Vehicle

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On the optimal control of manufacturing and remanufacturing activities with a single shared server

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2014年第1期234卷 86-98页

作者： Flapper, Simme Douwe Gayon, Jean-Philippe Lim, Lam Laurent Grenoble INP UJF Grenoble 1 CNRS G SCOP UMR5272 F-38031 Grenoble France Tech Univ Eindhoven NL-5600 MB Eindhoven Netherlands

We consider a single-product make-to-stock manufacturing-remanufacturing system. Returned products require remanufacturing before they can be sold. The manufacturing and remanufacturing operations are executed by the same single server, where switching from one activity to another does not involve time or cost and can be done at an arbitrary moment in time. Customer demand can be fulfilled by either newly manufactured or remanufactured products. The times for manufacturing and remanufacturing a product are exponentially distributed. Demand and used products arrive via mutually independent Poisson processes. Disposal of products is not allowed and all used products that are returned have to be accepted. Using Markov decision processes, we investigate the optimal manufacture-remanufacture policy that minimizes holding, backorder, manufacturing and remanufacturing costs per unit of time over an infinite horizon. For a subset of system parameter values we are able to completely characterize the optimal continuous-review dynamic preemptive policy. We provide an efficient algorithm based on quasi-birth-death processes to compute the optimal policy parameter values. For other sets of system parameter values, we present some structural properties and insights related to the optimal policy and the performance of some simple threshold policies. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Manufacturing Remanufacturing Shared server Inventory control stochastic dynamic programming

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Skeleton-Based Swarm Routing (SSR): Intelligent Smooth Routing for dynamic UAV Networks

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IEEE ACCESS 2021年 9卷 1286-1303页

作者： Toorchi, Niloofar Hu, Fei Bentley, Elizabeth Serena Kumar, Sunil Univ Alabama Dept Elect & Comp Engn Tuscaloosa AL 35487 USA US Air Force Res Lab Rome NY 13441 USA San Diego State Univ Elect & Comp Engn Dept San Diego CA 92182 USA

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.

关键词： Geometric routing quality of service reinforcement learning stochastic dynamic programming swarm networks UAV communication

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stochastic optimal control of manufacturing systems under production-dependent failure rates

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2014年 150卷 174-187页

作者： Francie, Kouedeu Annie Jean-Pierre, Kenne Pierre, Dejax Victor, Songmene Vladimir, Polotski Univ Quebec Ecole Technol Super Lab Integrated Prod Technol Dept Mech Engn Montreal PQ H3C 1K3 Canada Ecole Mines Nantes LUNAM F-44307 Nantes 3 France

A production system consisting of two parallel machines with production-dependent failure rates is investigated in this paper. The machines produce one type of final product and unmet demand is backlogged. The objective of the system is to find a productivity policy for both machines that will minimize the inventory and shortage costs over an infinite horizon. The failure rate of the main machine depends on its productivity, while the failure rate of the second machine is constant. In the proposed model, the main machine is characterized by a higher productivity. This paper proposes a stochastic dynamic programming formulation of the problem and derives the optimal policies numerically. A numerical example is included and sensitivity analyses with respect to the system parameters are examined to illustrate the importance and effectiveness of the proposed methodology. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Production planning stochastic dynamic programming Numerical methods

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Production planning optimization for manufacturing and remanufacturing system in stochastic environment

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JOURNAL OF INTELLIGENT MANUFACTURING 2013年第4期24卷 717-728页

作者： Naeem, Mohd Arshad Dias, Dean J. Tibrewal, Rupak Chang, P. C. Tiwari, M. K. Indian Inst Technol Dept Ind Engn & Management Kharagpur 721302 W Bengal India Coll Informat Dept Informat Management Chungli Taiwan

This article studies a single item dynamic lot sizing problem with manufacturing and remanufacturing provisions. The demands and returns are considered as both stochastic and deterministic. There are two inventories recoverable and serviceable inventory. We developed a dynamic programming based model with objective to determine the quantities that have to be manufactured or re-manufactured at each period in order to minimize the total cost, including production cost, holding cost for returns and finished goods, and backlog cost. Also, unit production cost is also taken as variable in case of deterministic case. Finally, a numerical example for each of deterministic and stochastic model is worked out to illustrate how the model is applied and to prove its feasibility.

关键词： Inventory control Manufacturing-remanufacturing hybrid system Lot sizing stochastic dynamic programming

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