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Solving the joint military medical evacuation problem via a random forest approximate dynamic programming approach

EXPERT SYSTEMS WITH APPLICATIONS 2023年 221卷

作者： Rodriguez, Channel A. Jenkins, Phillip R. Robbins, Matthew J. Air Force Inst Technol Dept Operat Sci 2950 Hobson Way Wright Patterson AFB OH 45433 USA

Members of the armed forces rely on having an effective and efficient medical evacuation (MEDEVAC) process for evacuating casualties from the battlefield to medical treatment facilities during combat operations. Determining which MEDEVAC units to task to respond to incoming requests for service, known as the MEDEVAC dispatching problem, is a natural sequential decision making problem and is the focus of this paper. Unlike previous research in this area, this paper incorporates triage classification errors and the possibility of having blood transfusion kits on board select MEDEVAC units. A discounted, infinite-horizon continuous-time Markov decision process model is formulated to examine the MEDEVAC dispatching problem and to compare generated dispatching policies to the currently practiced United States military policy. We utilize an approximate dynamic programming (ADP) technique that leverages a random forest value function approximation within an approximate policy iteration algorithmic framework to develop high-quality policies for both small-scale and large-scale problem instances. A representative planning scenario involving joint combat operations in South Korea is developed and utilized to investigate the differences between the various policies. Results from the analysis indicate that applying ADP techniques can improve current practices by as much as 29% with regard to a life-saving performance metric. This research is of particular interest to the military medical community and can inform the procedures of future military MEDEVAC operations.

关键词： Markov decision processes Medical evacuation approximate dynamic programming Policy iteration Random forest

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Real-time stochastic optimal scheduling of large-scale electric vehicles: A multidimensional approximate dynamic programming approach

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2020年 116卷 105542-000页

作者： Pan, Z. N. Yu, T. Chen, L. P. Yang, B. Wang, B. Guo, W. X. South China Univ Technol Coll Elect Power Guangzhou 510640 Guangdong Peoples R China Kunming Univ Sci & Technol Fac Elect Power Engn Kunming 650500 Yunnan Peoples R China Guangdong Power Grid Co Ltd Elect Dispatch & Control Ctr Guangzhou 510600 Guangdong Peoples R China

This paper studies the real-time charging optimization (RTCO) of large-scale electric vehicles (EVs), which is a multistage and multidimensional stochastic resource allocation problem. In order to handle the complex RTCO of large-scale EVs, a multidimensional approximate dynamic programming (ADP-RTCO) is designed for the sequent optimal decision makings. The hierarchy of ADP-RTCO contains two layers. In the upper layer, RTCO of large-scale EVs is formulated as a multidimensional energy storage problem by classifying EVs into several virtual EV clusters (EVCs). Then, temporal difference learning based policy iteration method is used to obtain the value function approximation for each EVC. In the lower layer, priority based reallocation algorithm is employed to obtain the detailed charging power for connected EVs. ADP-RTCO is designed with on-line learning ability to enhance its adaptability and robustness to ambiguous environment. Comprehensive simulation results demonstrate the optimality and robustness of ADP-RTCO in both cost-saving and load-flattening problems. Furthermore, ADP-RTCO can obtain higher-quality solution compared with other algorithms and is applicable for RTCO of large-scale EVs under uncertainties due to its high computation efficiency.

关键词： Electric vehicle Charging optimization Real-time optimization Stochastic optimization approximate dynamic programming

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An approximate dynamic programming algorithm for short-term electric vehicle fleet operation under uncertainty

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APPLIED ENERGY 2022年 325卷

作者： Lee, Sangmin Boomsma, Trine Krogh Univ Copenhagen Dept Math Sci DK-2100 Copenhagen Denmark Maersk R&D DK-1263 Copenhagen Denmark

This paper considers the dynamic problem of optimally operating a fleet of plug-in hybrid electric vehicles in a market environment. With uncertainty in future electricity prices and driving demands, we formulate a Markov decision process and determine a cost-minimizing policy for using the engine and charging and discharging the battery. As such, the policy is based on the trade-off between the costs of gasoline and electricity and between current and future power prices. To accommodate an inhomogeneous fleet composition and overcome the computational challenges of stochastic and dynamic optimization, including large-scale state and action spaces, we adopt the methodology of approximate dynamic programming. More specifically, using simulation and value function approximation by linear regression, we apply a least squares Monte Carlo method. This methodology allows for scaling with respect to fleet size and we are able to establish convergence of our algorithm for 100 vehicles by using 5000 samples in the simulation. Our results show that the vehicles should generally discharge the battery rather than using the engine unless battery capacity is insufficient to fully cover driving demand, but the timing of battery charging should be according to power prices. When comparing our policy to the simple policy of immediate charging, we demonstrate superiority for small and medium-sized fleets, with 2%-4% cost differences.

关键词： Plug-in hybrid vehicles approximate dynamic programming Least squares Monte Carlo

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An approximate dynamic programming Approach to Repeated Games with Vector Losses

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OPERATIONS RESEARCH 2024年第1期72卷 373页

作者： Kamble, Vijay Loiseau, Patrick Walrand, Jean Univ Illinois Dept Informat & Decis Sci Chicago IL 60607 USA Univ Grenoble Alpes Ctr Natl Rech Sci Inst Polytech Grenoble INRIALab Informat Grenoble F-38058 Grenoble France Max Planck Inst Software Syst D-66123 Saarbrucken Germany Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA

We describe an approximate dynamic programming (ADP) approach to compute approximations of the optimal strategies and of the minimal losses that can be guaranteed in discounted repeated games with vector-valued losses. Among other applications, such vector-valued games prominently arise in the analysis of worst-case regret in repeated decision making in unknown environments, also known as the adversarial online learning framework. At the core of our approach is a characterization of the lower Pareto frontier of the set of expected losses that a player can guarantee in these games as the unique fixed point of a set-valued dynamic programming operator. When applied to the problem of worst-case regret minimization with discounted losses, our approach yields algorithms that achieve markedly improved performance bounds compared with off-the-shelf online learning algorithms like Hedge. These results thus suggest the significant potential of ADP-based approaches in adversarial online learning.

关键词： vector repeated games online learning approximate dynamic programming

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An approximate dynamic programming Approach to dynamic Stochastic Matching

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INFORMS JOURNAL ON COMPUTING 2024年第4期36卷 1006-1022页

作者： You, Fan Vossen, Thomas Univ Colorado Leeds Sch Business Boulder CO 80309 USA

dynamic stochastic matching problems arise in a variety of recent applications, ranging from ridesharing and online video games to kidney exchange. Such problems are naturally formulated as Markov decision processes (MDPs) that are, however, intractable in general. To improve tractability, we investigate the linear programming -based approach to approximate dynamic programming. This approach can provide both feasible control policies and bounds on the MDPs' optimal policy value, which can be used to establish optimality gaps. However, the approximate linear programs (ALPs) resulting from this approach can often be difficult to solve. To address this computational challenge, we derive novel ALP reformulations that can be used for a broad class of dynamic stochastic matching problems that incorporate, among others, possible match failures and certain restrictions on feasible matchings. We show that these ALP reformulations can be solved efficiently and applied to a broad class of dynamic matching problems. In addition, our numerical results indicate that our ALP reformulations can produce tight bounds that allow us to establish near -optimal policy performance for a broad set of problem instances. Thus, ALP reformulations can present an attractive alternative for applications that involve dynamic stochastic matching.

关键词： matching approximate dynamic programming kidney exchange ridesharing matchmaking

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An approximate dynamic programming method for unit-based small hydropower scheduling

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FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Ji, Yueyang Wei, Hua Guangxi Univ Sch Elect Engn Nanning Peoples R China

Hydropower will become an important power source of China's power grids oriented to carbon neutral. In order to fully exploit the potential of water resources and achieve low-carbon operation, this paper proposes an approximate dynamic programming (ADP) algorithm for the unit-based short-term small hydropower scheduling (STSHS) framework considering the hydro unit commitment, which can accurately capture the physical and operational characteristics of individual units. Both the non-convex and non-linearization characteristics of the original STSHS model are retained without any linearization to accurately describe the hydropower production function and head effect, especially the dependence between the net head and the water volume in the reservoir, thereby avoiding loss of the actual optimal solution due to the large error introduced by the linearization process. An approximate value function of the original problem is formulated via the searching table model and approximate policy value iteration process to address the "curse of dimensionally " in traditional dynamic programming, which provides an approximate optimal strategy for the STSHS by considering both algorithm accuracy and computational efficiency. The model is then tested with a real-world instance of a hydropower plant with three identical units to demonstrate the effectiveness of the proposed method.

关键词： water small hydropower scheduling hydropower unit commitment approximate dynamic programming renewable energy sources

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A Monotone approximate dynamic programming Approach for the Stochastic Scheduling, Allocation, and Inventory Replenishment Problem: Applications to Drone and Electric Vehicle Battery Swap Stations

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TRANSPORTATION SCIENCE 2022年第4期56卷 1085-1110页

作者： Asadi, Amin Pinkley, Sarah Nurre Univ Twente Dept Ind Engn & Business Informat Syst NL-7522 NB Enschede Netherlands Univ Arkansas Dept Ind Engn Fayetteville AR 72701 USA

There is a growing interest in using electric vehicles (EVs) and drones for many applications. However, battery-oriented issues, including range anxiety and battery degradation, impede adoption. Battery swap stations are one alternative to reduce these concerns that allowthe swap of depleted for full batteries inminutes. We consider the problem of deriving actions at a battery swap station when explicitly considering the uncertain arrival of swap demand, battery degradation, and replacement. We model the operations at a battery swap station using a finite horizon Markov decision process model for the stochastic scheduling, allocation, and inventory replenishment problem(SAIRP), which determines when and how many batteries are charged, discharged, and replaced over time. We present theoretical proofs for the monotonicity of the value function and monotone structure of an optimal policy for special SAIRP cases. Because of the curses of dimensionality, we develop a new monotone approximate dynamic programming (ADP) method, which intelligently initializes a value function approximation using regression. In computational tests, we demonstrate the superior performance of the new regression-based monotone ADP method compared with exact methods and other monotone ADP methods. Furthermore, with the tests, we deduce policy insights for drone swap stations.

关键词： electric vehicles and drones battery swap station Markov decision processes battery degradation monotone policy and value function regression-based initialization approximate dynamic programming

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Decomposition and approximate dynamic programming approach to optimization of train timetable and skip-stop plan for metro networks

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2023年 157卷

作者： Yuan, Yin Li, Shukai Liu, Ronghui Yang, Lixing Gao, Ziyou Beijing Jiaotong Univ Sch Syst Sci Beijing 100044 Peoples R China Univ Leeds Inst Transport Studies Leeds LS2 9JT England

Carefully coordinating train timetables of different operating lines can help reduce transfer delays, which in turn reduces station crowding and improves overall service quality. This paper explores the optimization to train timetable and skip-stop plans that aims to minimize the total passenger waiting time and station crowding. The problem is formulated as a mixed integer non-linear programming model. To effectively address the complexity of the model, a decomposition and approximate dynamic programming approach is designed to convert the original network-level problem into a series of small-scale subproblems, one for each operating line, to be solved quickly in a distributed manner. The effectiveness and practicability of the model and algorithm are demonstrated on two case networks: a small-scale synthetic network of three metro lines and a real-world network based on Beijing metro. The computational results illustrate that the proposed strategy to generate train timetables and skip-stop plans can effectively reduce passenger waiting time and station crowing. The proposed decomposition and approximate dynamic programming approach is also shown to perform more efficiently than traditional heuristic algorithms, such as genetic algorithm and simulated annealing algorithm for large-scale networks.

关键词： Urban metro networks Timetable optimization Transfer coordination Skip-stop pattern approximate dynamic programming

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Improved approximate dynamic programming for real-time economic dispatch of integrated microgrids

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ENERGY 2022年 255卷

作者： Lin, Zhiyi Song, Chunyue Zhao, Jun Yin, Huan Zhejiang Univ State Key Lab Ind Control Technol Inst Ind Intelligence & Syst Engn Hangzhou 310027 Peoples R China China Mobile Zhejiang Innovat Res Inst Co Ltd Hangzhou 310030 Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Kowloon Clear Water Bay Hong Kong Peoples R China

Economic dispatch of electricity-heat microgrid is critical for real-time power generation and storage. However, conventional economic dispatch algorithms are generally integrated with static unit models without considering dynamics of units, thus leading to difficulties for real deployment in stochastical environments. In this paper, we propose a novel approximate dynamic programming (ADP) based realtime optimization algorithm. Specifically, the proposed ADP is employed to solve the Markov decision process with considering the dynamic process of combined-cycle gas turbine. Furthermore, we also design a novel weighted piecewise linear function to achieve the near-optimal solution, which is simple but effective for computational complexity reduction. In the experimental section, we conduct extensive experiments with comparisons to other economic dispatch methods. The experimental results indicate that: 1) The dynamic process of energy conversion brings more practical solutions;2) The proposed ADPbased method could handle the stochasticity of the microgrid;3) The proposed method outperforms the other intra-day optimization policies in both economical and computational efficiency. (c) 2022 Elsevier Ltd. All rights reserved.

关键词： Economic dispatch microgrid approximate dynamic programming dynamic process combined-cycle gas turbine

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An Improved approximate dynamic programming Method for The Integrated Fleet Sizing and Replenishment Planning Problem with Predetermined Delivery Frequencies

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IFAC-PapersOnLine 2022年第10期55卷 3034-3039页

作者： Duygu Aghazadeh Kadir Ertogral Industrial Engineering Department TOBB University of Economics and Technology Sogutozu cad. No:43 Ankara Turkey Mechanical and Industrial Engineering College of Engineering Qatar University Doha Qatar

In this paper we propose an Improved approximate dynamic programming approach for the integrated fleet sizing and replenishment planning problem utilizing fix and optimize heuristic. The problem is about deciding both the composition of a fleet for distributing a single item and the replenishment planning based on a predetermined delivery frequency. This problem integrates two important logistical issues namely fleet sizing and replenishment planning. The objective is minimizing all relevant costs composed of vehicle ownership, inventory, and approximate routing costs. We show the effectiveness of the proposed solution method on a set of fairly large size randomly generated problem instances.

关键词： Fleet sizing Replenishment planning approximate dynamic programming Fix optimize

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