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A proactive transfer policy for critical patient flow management

HEALTH CARE MANAGEMENT SCIENCE 2019年第2期22卷 287-303页

作者： Gonzalez, Jaime Ferrer, Juan-Carlos Cataldo, Alejandro Rojas, Luis Pontificia Univ Catolica Chile Sch Engn Vicuna Mackenna 4860 Santiago Chile Pontificia Univ Catolica Chile Sch Med Libertador Bernardo OHiggins 340 Santiago Chile

Hospital emergency departments are often overcrowded, resulting in long wait times and a public perception of poor attention. Delays in transferring patients needing further treatment increases emergency department congestion, has negative impacts on their health and may increase their mortality rates. A model built around a Markov decision process is proposed to improve the efficiency of patient flows between the emergency department and other hospital units. With each day divided into time periods, the formulation estimates bed demand for the next period as the basis for determining a proactive rather than reactive transfer decision policy. Due to the high dimensionality of the optimization problem involved, an approximate dynamic programming approach is used to derive an approximation of the optimal decision policy, which indicates that a certain number of beds should be kept free in the different units as a function of the next period demand estimate. Testing the model on two instances of different sizes demonstrates that the optimal number of patient transfers between units changes when the emergency patient arrival rate for transfer to other units changes at a single unit, but remains stable if the change is proportionally the same for all units. In a simulation using real data for a hospital in Chile, significant improvements are achieved by the model in key emergency department performance indicators such as patient wait times (reduction higher than 50%), patient capacity (21% increase) and queue abandonment (from 7% down to less than 1%).

关键词： Markov decision process approximate dynamic programming Emergency department Critical care beds Patient flow

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Strategic Location and Dispatch Management of Assets in a Military Medical Evacuation Enterprise

Strategic Location and Dispatch Management of Assets in a Mi...

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作者： Phillip R. Jenkins Air University

学位级别：博士

This dissertation considers the importance of optimizing deployed military med- ical evacuation (MEDEVAC) systems and utilizes operations research techniques to develop models that allow military medical planners to analyze different strategies regarding the management of MEDEVAC assets in a deployed environment. For op- timization models relating to selected subproblems of the MEDEVAC enterprise, the work herein leverages integer programming, multi-objective optimization, Markov de- cision processes (MDPs), approximate dynamic programming (ADP), and machine learning, as appropriate, to identify relevant insights for aerial MEDEVAC operations. This research is conducted in the form of three related research components: the first component develops models for optimal MEDEVAC location within an enterprise of assets, whereas the second and third components seek optimal MEDEVAC dis- patching policies for a set of established assets via a sequence of models of increasing operational complexity and corresponding solution methods. Determining where to locate mobile aeromedical staging facilities (MASFs) as well as identifying how many aeromedical helicopters to allocate to each MASF, commonly referred to as the MEDEVAC location-allocation problem, is vital to the success of a deployed MEDEVAC system. An integer mathematical programming formulation is developed to determine the location and allocation of MEDEVAC as- sets over the phases of a military deployment to support operations ranging from peacekeeping through combat to post-combat resolution. The model seeks to address the multi-objective problem of maximizing the total expected coverage of demand as a measure of solution effectiveness, minimizing the maximum number of located MASFs in any deployment phase as a measure of solution efficiency, and minimizing the total number of MASF relocations throughout the deployment as a measure of solution robustness. Moreover, the model utilizes the ε-constraint Method to assess

关键词： Military medical evacuation (MEDEVAC) integer programming multi-objective optimization Markov decision process approximate dynamic programming machine learning

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Adaptive Optimal Control via Continuous-Time Q-Learning for Unknown Nonlinear Affine Systems 58

Adaptive Optimal Control via Continuous-Time Q-Learning for ...

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58th IEEE Conference on Decision and Control (CDC)

作者： Chen, Anthony Siming Herrmann, Guido Univ Bristol Dept Mech Engn Bristol BS8 1QU Avon England Univ Manchester Dept Elect & Elect Engn Manchester M13 9PL Lancs England

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113982

This paper proposes two novel adaptive optimal control algorithms for continuous-time nonlinear affine systems based on reinforcement learning: i) generalised policy iteration (GPI) and ii) Q-learning. As a result, the a priori knowledge of the system drift f(x) is not needed via GPI, which gives us a partially model-free and online solution. We then for the first time extend the idea of Q-learning to the nonlinear continuous time optimal control problem in a noniterative manner. This leads to a completely model-free method where neither the system drift f(x) nor the input gain g(x) is needed. For both methods, the adaptive critic and actor are continuously and simultaneously updating each other without iterative steps, which effectively avoids the hybrid structure and the need for an initial stabilising control policy. Moreover, finite-time convergence is guaranteed by using a sliding mode technique in the new adaptive approach, where the persistent excitation (PE) condition can be directly verified online. We also prove the overall Lyapunov stability and demonstrate the effectiveness of the proposed algorithms using numerical examples.

关键词： adaptive optimal control Q-learning non-linear systems reinforcement learning approximate dynamic programming adaptive critic

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A hierarchical learning control framework for tracking tasks, based on model-free principles 23

A hierarchical learning control framework for tracking tasks...

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23rd International Conference on System Theory, Control and Computing (ICSTCC)

作者： Radac, Mircea-Bogdan Negru, Vlad Precup, Radu-Emil Politehn Univ Timisoara PUT AAI Dept Timisoara Romania PUT AAI Dept Timisoara Romania

ISBN: (纸本)9781728106991

A hierarchical tracking learning framework is proposed in this work, by which, an optimally learned tracking behavior is extrapolated to new unseen trajectories without the need for relearning. This intelligent behavior uses learned reference inputs controlled outputs pairs called primitives, over a feedback control system (CS). Learning is based on model-free Iterative Learning Control under linearity assumption of the underlying CS. The CS linearity is indirectly ensured at low-level through an output reference model (ORM) tracking neural network state-feedback controller learned with an iterative model-free approximate value iteration as a representative reinforcement learning approach. Learning uses a large amount of input-output process data collected with an underperforming linear controller, but designed from fewer input-output data, in a model-free VRFT approach. The higher-level primitive-based learning is validated on a coupled highly-dimensional nonlinear aerodynamic positioning system. It proves that the optimal tracking behavior is extendable to new unseen trajectories, without relearning.

关键词： data-driven model-free control learning control neural networks Virtual Reference Feedback Tuning approximate dynamic programming reinforcement learning Iterative Learning Control hierarchical control primitive-based control

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Capacity expansion under regulatory uncertainty: A real options-based study in international container shipping

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2018年 113卷 75-93页

作者： Haehl, Christian Spinier, Stefan Kuehne Fdn Endowed Chair Logist Management WHU Otto Beisheim Sch Management Burgpl 2 D-56179 Vallendar Germany

We present a real options model for capacity expansion that introduces uncertainty about potential future regulation (regulatory uncertainty) and key characteristics of capacity decisions: investment with time to build, divestment, the option to charter, and operating flexibility. Regulatory uncertainty is modelled as a possible jump in operating, investment, and charter costs during the simulation horizon. We find that regulatory uncertainty with grandfathering (the extant fleet is exempt from compliance) promotes high up-front investment leading to excess capacity and increased emissions. However, regulatory uncertainty without grandfathering reduces investment and emissions and the use of more flexible capacity options, such as chartering.

关键词： Container shipping Regulatory uncertainty Environmental regulation approximate dynamic programming Investment analysis Capacity expansion

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dynamic Orienteering on a Network of Queues

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TRANSPORTATION SCIENCE 2018年第3期52卷 691-706页

作者： Zhang, Shu Ohlmann, Jeffrey W. Thomas, Barrett W. Chongqing Univ Sch Econ & Business Adm Chongqing 400044 Peoples R China Univ Iowa Dept Management Sci Tippie Coll Business Iowa City IA 52242 USA

We introduce a stochastic orienteering problem on a network of queues in which the traveler must arrive and enter service at locations within the respective time windows to collect rewards, but the traveler may experience stochastic wait time at each location before service can begin. To maximize the expected rewards collected, the traveler must determine which locations to visit and how long to wait in queues at each location. We formally model the problem as a Markov decision process with the objective of maximizing the expected collected rewards. We investigate the existence of optimal control limits and examine conditions under which certain actions cannot be optimal. To solve the problem, we propose an approximate dynamic programming approach based on rollout algorithms. The method introduces a two-stage heuristic estimation that we refer to as compound rollout. In the first stage, the algorithm decides whether to stay at the current location or go to another location. If departing the current location, it chooses the next location in the second stage. We demonstrate the value of our modeling and solution approaches by comparing the dynamic policies to a-priori-route solutions with recourse actions.

关键词： stochastic orienteering dynamic routing time windows queueing approximate dynamic programming rollout algorithms

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A Least-Squares Temporal Difference based method for solving resource allocation problems

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IFAC JOURNAL OF SYSTEMS AND CONTROL 2020年 13卷

作者： Forootani, Ali Tipaldi, Massimo Zarch, Majid Ghaniee Liuzza, Davide Glielmo, Luigi Univ Sannio Dept Engn Piazza Roma I-82100 Benevento Italy Bu Ali Sina Univ Dept Elect Engn Hamadan Hamadan Iran ENEA Fus & Nucl Safety Dept Rome Italy

Value function approximation has a central role in approximate dynamic programming (ADP) to overcome the so-called curse of dimensionality associated to real stochastic processes. In this regard, we propose a novel Least-Squares Temporal Difference (LSTD) based method: the ``Multi-trajectory Greedy LSTD'' (MG-LSTD). It is an exploration-enhanced recursive LSTD algorithm with the policy improvement embedded within the LSTD iterations. It makes use of multi-trajectories Monte Carlo simulations in order to enhance the system state space exploration. This method is applied for solving resource allocation problems modeled via a constrained Stochastic dynamic programming (SDP) based framework. In particular, such problems are formulated as a set of parallel Birth-Death Processes (BDPs). Some operational scenarios are defined and solved to show the effectiveness of the proposed approach. Finally, we provide some experimental evidence on the MG-LSTD algorithm convergence properties in function of its key-parameters. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Least-squares temporal difference approximate dynamic programming Markov decision process Birth-death process Monte Carlo simulations

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Discrete-Time Stable Generalized Self-Learning Optimal Control With Approximation Errors

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2018年第4期29卷 1226-1238页

作者： Wei, Qinglai Li, Benkai Song, Ruizhuo Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China

In this paper, a generalized policy iteration (GPI) algorithm with approximation errors is developed for solving infinite horizon optimal control problems for nonlinear systems. The developed stable GPI algorithm provides a general structure of discrete-time iterative adaptive dynamic programming algorithms, by which most of the discrete-time reinforcement learning algorithms can be described using the GPI structure. It is for the first time that approximation errors are explicitly considered in the GPI algorithm. The properties of the stable GPI algorithm with approximation errors are analyzed. The admissibility of the approximate iterative control law can be guaranteed if the approximation errors satisfy the admissibility criteria. The convergence of the developed algorithm is established, which shows that the iterative value function is convergent to a finite neighborhood of the optimal performance index function, if the approximate errors satisfy the convergence criterion. Finally, numerical examples and comparisons are presented.

关键词： Adaptive critic designs adaptive dynamic programming (ADP) approximate dynamic programming generalized policy iteration (GPI) neural networks neurodynamic programming nonlinear systems optimal control reinforcement learning

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Discrete-Time Local Value Iteration Adaptive dynamic programming: Convergence Analysis

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2018年第6期48卷 875-891页

作者： Wei, Qinglai Lewis, Frank L. Liu, Derong Song, Ruizhuo Lin, Hanquan Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Texas Arlington UTA Res Inst Arlington TX 76118 USA Northeastern Univ Shenyang 110036 Liaoning Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China

In this paper, convergence properties are established for the newly developed discrete-time local value iteration adaptive dynamic programming (ADP) algorithm. The present local iterative ADP algorithm permits an arbitrary positive semidefinite function to initialize the algorithm. Employing a state-dependent learning rate function, for the first time, the iterative value function and iterative control law can be updated in a subset of the state space instead of the whole state space, which effectively relaxes the computational burden. A new analysis method for the convergence property is developed to prove that the iterative value functions will converge to the optimum under some mild constraints. Monotonicity of the local value iteration ADP algorithm is presented, which shows that under some special conditions of the initial value function and the learning rate function, the iterative value function can monotonically converge to the optimum. Finally, three simulation examples and comparisons are given to illustrate the performance of the developed algorithm.

关键词： Adaptive critic designs adaptive dynamic programming (ADP) approximate dynamic programming local iteration neural networks neuro-dynamic programming nonlinear systems optimal control

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Novel Adaptive Saturated Attitude Tracking Control of Rigid Spacecraft with Guaranteed Transient and Steady-State Performance

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JOURNAL OF AEROSPACE ENGINEERING 2018年第5期31卷 04018062-04018062页

作者： Yin, Zeyang Luo, Jianjun Wei, Caisheng Northwestern Polytech Univ Sch Astronaut Natl Key Lab Aerosp Flight Dynam Youyi West St Xian 710072 Shaanxi Peoples R China

In this paper, a novel adaptive model-free attitude tracking control method is investigated for rigid spacecraft with consideration of the external disturbance, unknown inertia matrix, and input saturation. First, the considered attitude tracking system with input saturation is transformed into a Lagrangian model, and a dead zone-based model is used to describe the saturation nonlinearity. Second, using the prescribed performance control theory, a static prescribed performance attitude control scheme is presented, by which the transient and steady-state performance (including the convergence rate, overshoot, and boundedness) of the attitude tracking system is proved to be guaranteed. Third, in order to improve the performance of the static prescribed performance control scheme, a novel learning-based supplementary control scheme is presented based on the approximate dynamic programming. Finally, two groups of numerical simulations are used to illustrate the effectiveness of the proposed learning-based prescribed performance attitude control method.

关键词： Prescribed performance Spacecraft approximate dynamic programming Neural networks Input saturation

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