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approximate dynamic programming for the military aeromedical evacuation dispatching, preemption-rerouting, and redeployment problem

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第1期290卷 132-143页

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

Military medical planners must consider how aeromedical evacuation (MEDEVAC) assets will be utilized when preparing for and supporting combat operations. This research examines the MEDEVAC dispatching, preemption-rerouting, and redeployment (DPR) problem. The intent of this research is to determine high-quality DPR policies that improve the performance of United States Army MEDEVAC systems and ultimately increase the combat casualty survivability rate. A discounted, infinite-horizon Markov decision process (MDP) model of the MEDEVAC DPR problem is formulated and solved via an approximate dynamic programming (ADP) strategy that utilizes a support vector regression value function approximation scheme within an approximate policy iteration algorithmic framework. The objective is to maximize the expected total discounted reward attained by the system. The applicability of the MDP model is examined via a notional, representative planning scenario based on high-intensity combat operations to defend Azerbaijan against a notional aggressor. Computational experimentation is performed to determine how selected problem features and algorithmic features affect the quality of solutions attained by the ADP-generated DPR policies and to assess the efficacy of the proposed solution methodology. The results from the computational experiments indicate the ADP-generated policies significantly outperform the two benchmark policies considered. Moreover, the results reveal that the average service time of high-precedence, time-sensitive requests decreases when an ADP policy is adopted during high-intensity conflicts. As the rate at which requests enter the MEDEVAC system increases, the performance gap between the ADP policy and the first benchmark policy (i.e., the currently practiced, closest-available dispatching policy) increases substantially. Conversely, as the rate at which requests enter the system decreases, the ADP performance improvement over both benchmark policies decreases,

关键词： OR in defense approximate dynamic programming Markov decision process Support vector regression Military MEDEVAC

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Electric vehicle fleet charging management: An approximate dynamic programming policy

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European Journal of Operational Research 2025年

作者： Mahyari, Ehsan Freeman, Nickolas Department of Operations and Analytics University of Scranton Scranton 18510 PA United States Department of Information Systems Statistics and Management Science University of Alabama Tuscaloosa 35487 AL United States

The growing prevalence of electric vehicles (EVs) requires efficient charging management strategies to tackle the challenges associated with their integration into the power grid. This requirement is particularly true for Charging-as-a-Service (CaaS) providers, who manage charging services for fleet operators in exchange for a fixed service fee. Incorporating uncertainty into optimization models for this dynamic environment further complicates the associated optimization problem, which falls into the NP-hard class. This research introduces an innovative approximate dynamic programming (ADP) policy for managing the charging of EV fleets at a charging depot equipped with diverse multi-connector chargers. A feature mapping analysis identifies critical system features that shape the future costs of a decision. A comparative analysis illustrates the effectiveness of the proposed policy in terms of cost reduction and service level. Moreover, we observe significant reductions in computation time when updating charging decisions compared to a two-stage rule-based model developed as a benchmark. In addition to benefits for EV fleet operators and CaaS providers, the proposed policy contributes to power grid sustainability by reducing charge load during peak hours, thereby enhancing overall grid stability and efficiency. © 2025 The Authors

关键词： approximate dynamic programming Electric vehicle fleet charging Markov decision process

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approximate dynamic programming Based Optimal Control Applied to an Integrated Plant with a Reactor and a Distillation Column with Recycle

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AICHE JOURNAL 2009年第4期55卷 919-930页

作者： Tosukhowong, Thidarat Lee, Jay H. Georgia Inst Technol Sch Chem & Biomol Engn Atlanta GA 30332 USA

An approximate dynamic programming (ADP) method has shown good performance in solving optimal control problems in many small-scale process control applications. The offline computational procedure of ADP constructs an approximation of the optimal "cost-to-go" function, which parameterizes the optimal control policy with respect to the state variable. With the approximate "cost-to-go" function computed, a multi-stage optimization problem that needs to be solved online at every sample time can be reduced to a single-stage optimization, thereby significantly lessening the real-time computational load. Moreover, stochastic uncertainties can be addressed relatively easily within this framework. Nonetheless, the existing ADP method requires excessive offline computation when applied to a high-dimensional system. A case study of a reactor and a distillation column with recycle was used to illustrate this issue. Then, several ways were proposed to reduce the computational load so that the ADP method can be applied to high-dimensional integrated plants. The results showed that the approach is much more superior to NMPC in both deterministic and stochastic cases. (C) 2009 American Institute of Chemical Engineers AIChE J, 55: 919-930, 2009

关键词： approximate dynamic programming nonlinear optimal control integrated plant dynamic optimization nonlinear model predictive control

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approximate dynamic programming for Nonlinear-Constrained Optimizations

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IEEE TRANSACTIONS ON CYBERNETICS 2021年第5期51卷 2419-2432页

作者： Yang, Xiong He, Haibo Zhong, Xiangnan Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Univ Rhode Isl Dept Elect Comp & Biomed Engn Kingston RI 02881 USA Univ North Texas Dept Elect Engn Denton TX 76207 USA

In this paper, we study the constrained optimization problem of a class of uncertain nonlinear interconnected systems. First, we prove that the solution of the constrained optimization problem can be obtained through solving an array of optimal control problems of constrained auxiliary subsystems. Then, under the framework of approximate dynamic programming, we present a simultaneous policy iteration (SPI) algorithm to solve the Hamilton-Jacobi-Bellman equations corresponding to the constrained auxiliary subsystems. By building an equivalence relationship, we demonstrate the convergence of the SPI algorithm. Meanwhile, we implement the SPI algorithm via an actor-critic structure, where actor networks are used to approximate optimal control policies and critic networks are applied to estimate optimal value functions. By using the least squares method and the Monte Carlo integration technique together, we are able to determine the weight vectors of actor and critic networks. Finally, we validate the developed control method through the simulation of a nonlinear interconnected plant.

关键词： Optimal control Interconnected systems Optimization Nonlinear systems dynamic programming Approximation algorithms approximate dynamic programming constrained optimization neural networks nonlinear interconnected system optimal control

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approximate dynamic programming for capacity allocation in the service industry

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2012年第1期218卷 239-250页

作者： Schuetz, Hans-Joerg Kolisch, Rainer Tech Univ Munich TUM Sch Management D-80333 Munich Germany

We consider a problem where different classes of customers can book different types of service in advance and the service company has to respond immediately to the booking request confirming or rejecting it. The objective of the service company is to maximize profit made of class-type specific revenues, refunds for cancellations or no-shows as well as cost of overtime. For the calculation of the latter, information on the underlying appointment schedule is required. In contrast to most models in the literature we assume that the service time of clients is stochastic and that clients might be unpunctual. Throughout the paper we will relate the problem to capacity allocation in radiology services. The problem is modeled as a continuous-time Markov decision process and solved using simulation-based approximate dynamic programming (ADP) combined with a discrete event simulation of the service period. We employ an adapted heuristic ADP algorithm from the literature and investigate on the benefits of applying ADP to this type of problem. First, we study a simplified problem with deterministic service times and punctual arrival of clients and compare the solution from the ADP algorithm to the optimal solution. We find that the heuristic ADP algorithm performs very well in terms of objective function value, solution time, and memory requirements. Second, we study the problem with stochastic service times and unpunctuality. It is then shown that the resulting policy constitutes a large improvement over an "optimal" policy that is deduced using restrictive, simplifying assumptions. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Capacity allocation Services Health care operations approximate dynamic programming Reinforcement learning Semi-Markov decision process

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approximate dynamic programming for pickup and delivery problem with crowd-shipping

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TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2024年 187卷

作者： Mousavi, Kianoush Bodur, Merve Cevik, Mucahit Roorda, Matthew J. Univ Toronto Dept Civil & Mineral Engn Toronto ON Canada Univ Edinburgh Sch Math Edinburgh Scotland Toronto Metropolitan Univ Dept Mech Ind & Mechatron Engn Toronto ON Canada

We study a variant of dynamic pickup and delivery crowd-shipping operation for delivering online orders within a few hours from a brick-and-mortar store. This crowd-shipping operation is subject to a high degree of uncertainty due to the stochastic arrival of online orders and crowd- shippers that impose several challenges for efficient matching of orders to crowd-shippers. We formulate the problem as a Markov decision process and develop an approximate dynamic programming (ADP) policy using value function approximation for obtaining a highly scalable and real-time matching strategy while considering temporal and spatial uncertainty in arrivals of online orders and crowd-shippers. We incorporate several algorithmic enhancements to the ADP algorithm, which significantly improve the convergence. We compare the ADP policy with an optimization-based myopic policy using various performance measures. Our numerical analysis with varying parameter settings shows that ADP policies can lead to up to 25.2% cost savings and a 9.8% increase in the number of served orders. Overall, we find that our proposed framework can guide crowd-shipping platforms for efficient real-time matching decisions and enhance the platform delivery capacity.

关键词： Crowd-shipping Last-mile delivery Markov decision process approximate dynamic programming Value function approximation

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approximate dynamic programming algorithms for optimal dosage decisions in controlled ovarian hyperstimulation

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2012年第2期222卷 328-340页

作者： He, Miao Zhao, Lei Powell, Warren B. Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China Princeton Univ Dept Operat Res & Financial Engn Princeton NJ 08544 USA

In the controlled ovarian hyperstimulation (COH) treatment, clinicians monitor the patients' physiological responses to gonadotropin administration to tradeoff between pregnancy probability and ovarian hyperstimulation syndrome (OHSS). We formulate the dosage control problem in the COH treatment as a stochastic dynamic program and design approximate dynamic programming (ADP) algorithms to overcome the well-known curses of dimensionality in Markov decision processes (MDP). Our numerical experiments indicate that the piecewise linear (PWL) approximation ADP algorithms can obtain policies that are very close to the one obtained by the MDP benchmark with significantly less solution time. (c) 2012 Elsevier B.V. All rights reserved.

关键词： OR in health services approximate dynamic programming Controlled ovarian hyperstimulation Ovarian hyperstimulation syndrome

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approximate dynamic programming for Two-player Zero-sum Game Related to H_∞ Control of Unknown Nonlinear Continuous-time Systems

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2015年第1期13卷 99-109页

作者： Yasini, Sholeh Bagher, Mohammad Sistani, Naghibi Karimpour, Ali Ferdowsi Univ Mashhad Dept Elect Engn Mashhad 917551111 Iran

This paper develops a concurrent learning-based approximate dynamic programming (ADP) algorithm for solving the two-player zero-sum (ZS) game arising in H-infinity control of continuous-time (CT) systems with unknown nonlinear dynamics. First, the H-infinity control is formulated as a ZS game and then, an online algorithm is developed that learns the solution to the Hamilton-Jacobi-Isaacs (HJI) equation without using any knowledge on the system dynamics. This is achieved by using a neural network (NN) identifier to approximate the uncertain system dynamics. The algorithm is implemented on actor-critic-disturbance NN structure along with the NN identifier to approximate the optimal value function and the corresponding Nash solution of the game. All NNs are tuned at the same time. By using the idea of concurrent learning the need to check for the persistency of excitation condition is relaxed to simplified condition. The stability of the overall system is guaranteed and the convergence to the Nash solution of the game is shown. Simulation results show the effectiveness of the algorithm.

关键词： approximate dynamic programming concurrent learning H-infinity control neural networks two-player zero-sum game unknown dynamics

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approximate dynamic programming based control of hyperbolic PDE systems using reduced-order models from method of characteristics

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COMPUTERS & CHEMICAL ENGINEERING 2013年 57卷 122-132页

作者： Munusamy, Sudhakar Narasimhan, Sridharakumar Kaisare, Niket S. Indian Inst Technol Dept Chem Engn Madras 600036 Tamil Nadu India

approximate dynamic programming (ADP) is a model based control technique suitable for nonlinear systems. Application of ADP to distributed parameter systems (DPS) which are described by partial differential equations is a computationally intensive task. This problem is addressed in literature by the use of reduced order models which capture the essential dynamics of the system. Order reduction of DPS described by hyperbolic PDEs is a difficult task as such systems exhibit modes of nearly equal energy. The focus of this contribution is ADP based control of systems described by hyperbolic PDEs using reduced order models. Method of characteristics (MOC) is used to obtain reduced order models. This reduced order model is then used in ADP based control for solving the set-point tracking problem. Two case studies involving single and double characteristics are studied. Open loop simulations demonstrate the effectiveness of MOC in reducing the order and the closed loop simulations with ADP based controller indicate the advantage of using these reduced order models. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Method of characteristics approximate dynamic programming Distributed parameter system Hyperbolic PDE Model based control

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approximate dynamic programming based supplementary reactive power control for DFIG wind farm to enhance power system stability

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NEUROCOMPUTING 2015年 170卷 417-427页

作者： Guo, Wentao Liu, Feng Si, Jennie He, Dawei Harley, Ronald Mei, Shengwei Tsinghua Univ Dept Elect Engn State Key Lab Power Syst Beijing 100084 Peoples R China Arizona State Univ Dept Elect Engn Tempe AZ 85287 USA Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

Reactive power control of doubly fed induction generators (DFIGs) has been a heated topic in transient stability control of power systems in recent years. By using a new online supplementary learning control (OSLC) approach based on the theory of approximate dynamic programming (ADP), this paper develops an optimal and adaptive design method for the supplementary reactive power control of DFIGs to improve transient stability of power systems. To augment the reactive power command of the rotor-side converter (RSC), a supplementary controller is designed to reduce voltage sag at the common coupling point during a fault, and to mitigate active power oscillation of the wind farm after a fault. As a result, the transient stability of both DFIGs and the power system is enhanced. For the supplementary controller design, an action dependent cost function is introduced to make the OSLC model-free and completely data-driven. Furthermore, a least-squares based policy iteration algorithm is employed to train the supplementary controller with convergence and stability guarantee. By using such techniques, the supplementary reactive power controller can be trained directly from data measurements, and therefore, can adapt to system or external changes without an explicit offline system identification process. Simulations carried out in Power System Computer Aided Design/ Electro Magnetic Transient in DC System (PSCAD/EMTDC) show that the OSLC based supplementary reactive power controller can significantly improve the transient performance of the wind farm and enhance the transient stability of the power system after sever faults. (C) 2015 Elsevier B.V. All rights reserved.

关键词： approximate dynamic programming Doubly fed induction generator (DFIG) Reactive power control Online supplementary learning control

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