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New closed-loop approximate dynamic programming for solving stochastic decentralized multi-project scheduling problem with resource transfers

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 185卷 115593-115593页

作者： Zhao, Song Xu, Zhe Beihang Univ Sch Econ & Management Beijing 100191 Peoples R China

Resource transfers and uncertain activity durations are two practical factors that are difficult to cope with in real project management. This paper studies a new decentralized multi-project scheduling problem with both resource transfers and uncertain activity durations, we name it the stochastic decentralized multi-project scheduling problem with resource transfers (SDRCMPSPTT). To tackle the curse-of-dimensionality of an exact solution approach, we develop a new and effective rollout policy-based approximate dynamic programming (ADP) algorithm for SDRCMPSPTT. Based on the benchmark instances, we build a new data set and examine the performance of 12 priority rule heuristics, then select the two best ones as the base policy for our rollout algorithm. The proposed approach is verified under a stochastic environment by assessing different base heuristic policies, computational results show that the rollout algorithm can further improve the solution quality of the base heuristics. Compared with the state-of-the-art algorithm for solving the decentralized multi project scheduling problem under a deterministic environment, our rollout policy-based ADP algorithm can also obtain competitive solutions.

关键词： Decentralized multi-project scheduling Resource transfers Uncertain activity durations approximate dynamic programming Rollout algorithm

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Large-scale dynamic system optimization using dual decomposition method with approximate dynamic programming

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SYSTEMS & CONTROL LETTERS 2021年 150卷 104894-104894页

作者： Rokhforoz, Pegah Kebriaei, Hamed Ahmadabadi, Majid Nili Univ Tehran Sch Elect & Comp Engn Coll Engn Tehran Iran Inst Res Fundamental Sci IPM Sch Comp Sci POB 19395-5746 Tehran Iran

In this paper, multi-agent dynamic optimization with a coupling constraint is studied. The aim is to minimize a strongly convex social cost function, by considering a linear stochastic dynamics for each agent and also coupling constraints among the agents. In order to handle the coupling constraint and also, to avoid high computational cost imposed by a centralized method for large scale systems, the dual decomposition method is used to decompose the problem into multiple individual sub-problems, while the dual variable is adjusted by a coordinator. Nevertheless, since each sub-problem is not a linear-quadratic (LQ) optimal control problem, and hence its closed-form solution does not exist, approximate dynamic programming (ADP) is utilized to solve the sub-problems. The main contribution of the paper is to propose an algorithm by considering the interrelated iterations of dual variable adjustment and ADP, and to prove the convergence of the algorithm to the global optimal solution of the social cost function. Additionally, the implementation of the proposed algorithm using a neural network is presented. Also, the computational advantage of the proposed algorithm in comparison with other bench-marking methods is discussed in simulation results. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Multi-agent system approximate dynamic programming Duality theory Coupling constraint

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Robust Adaptive Control with Active Learning for Fed-Batch Process based on approximate dynamic programming 21st

Robust Adaptive Control with Active Learning for Fed-Batch P...

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21st IFAC World Congress on Automatic Control - Meeting Societal Challenges

作者： Byun, Ha-Eun Kim, Boeun Lee, Jay H. Korea Adv Inst Sci & Technol Dept Chem & Biomol Engn 291 Daehak Ro Daejeon 34141 South Korea Univ Wisconsin Dept Chem & Biol Engn Madison WI 53706 USA

Batch process is often subject to a high degree of uncertainty in raw material quality and other initial feedstock conditions. One of the key objectives in operating a batch process is achieving consistent performance and constraint satisfaction in the presence of these uncertainties This study presents a method for optimal control of a fed-batch process, which can actively and robustly cope with system uncertainty. As in dual control, the method aims to achieve an optimal balance between control actions (exploitation) and probing actions (exploration), leading to improved process performance by actively reducing system uncertainty. An optimal solution of the dual control problem can be found by stochastic dynamic programming but it is computationally intractable in most practical cases. In this study, an approximate dynamic programming (ADP) method for solving the dual control problem is tailored to a batch process which involves non-stationary and nonlinear dynamics Rewards are formulated to maximize a given end objective while satisfying path constraints. Performance of the ADP-based dual controller is tested on a fed batch bioreactor with two uncertain parameters. Copyright (C) 2020 The Authors.

关键词： Robust adaptive control Dual control Stochastic optimal control approximate dynamic programming Fed-batch process Model uncertainty

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A review of approximate dynamic programming applications within military operations research

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OPERATIONS RESEARCH PERSPECTIVES 2021年 8卷

作者： Rempel, M. Cai, J. Def Res & Dev Canada Ctr Operat Res & Anal 101 Colonel By Dr Ottawa ON K1A 0K2 Canada Canadian Joint Operat Command 1600 Star Top Rd Ottawa ON K1B 3W6 Canada

Sequences of decisions that occur under uncertainty arise in a variety of settings, including transportation, communication networks, finance, defence, etc. The classic approach to find an optimal decision policy for a sequential decision problem is dynamic programming;however its usefulness is limited due to the curse of dimensionality and the curse of modelling, and thus many real-world applications require an alternative approach. Within operations research, over the last 25 years the use of approximate dynamic programming (ADP), known as reinforcement learning in many disciplines, to solve these types of problems has increased in popularity. These efforts have resulted in the successful deployment of ADP-generated decision policies for driver scheduling in the trucking industry, locomotive planning and management, and managing high value spare parts in manufacturing. In this article we present the first review of applications of ADP within a defence context, specifically focusing on those which provide decision support to military or civilian leadership. This article's main contributions are twofold. First, we review 18 decision support applications, spanning the spectrum of force development, generation, and employment, that use an ADP-based strategy and for each highlight how its ADP algorithm was designed, evaluated, and the results achieved. Second, based on the trends and gaps identified we discuss five topics relevant to applying ADP to decision support problems within defence: the classes of problems studied;best practices to evaluate ADP-generated policies;advantages of designing policies that are incremental versus complete overhauls when compared to currently practiced policies;the robustness of policies as scenarios change, such as a shift from high to low intensity conflict;and sequential decision problems not yet studied within defence that may benefit from ADP.

关键词： Sequential decision problem Markov decision process approximate dynamic programming Reinforcement learning Military

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dynamic Energy Management of a Microgrid Using approximate dynamic programming and Deep Recurrent Neural Network Learning

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IEEE TRANSACTIONS ON SMART GRID 2019年第4期10卷 4435-4445页

作者： Zeng, Peng Li, Hepeng He, Haibo Li, Shuhui Chinese Acad Sci Shenyang Inst Automat Lab Networked Control Syst Shenyang 110016 Liaoning Peoples R China Univ Rhode Isl Dept Elect Comp & Biomed Engn Kingston RI 02881 USA Univ Alabama Dept Elect & Comp Engn Tuscaloosa AL 35487 USA

This paper focuses on economical operation of a microgrid (MG) in real-time. A novel dynamic energy management system is developed to incorporate efficient management of energy storage system into MG real-time dispatch while considering power flow constraints and uncertainties in load, renewable generation and real-time electricity price. The developed dynamic energy management mechanism does not require long-term forecast and optimization or distribution knowledge of the uncertainty, but can still optimize the long-term operational costs of MGs. First, the real-time scheduling problem is modeled as a finite-horizon Markov decision process over a day. Then, approximate dynamic programming and deep recurrent neural network learning are employed to derive a near optimal real-time scheduling policy. Last, using real power grid data from California independent system operator, a detailed simulation study is carried out to validate the effectiveness of the proposed method.

关键词： Microgrid dynamic energy management system approximate dynamic programming recurrent neural network deep learning

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A new approximate dynamic programming algorithm based on an actor?critic framework for optimal control of alkali?surfactant?polymer flooding

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ENGINEERING OPTIMIZATION 2019年第12期51卷 2147-2168页

作者： Li, Shurong Han, Lu Ge, Yulei Shi, Yuhuan Beijing Univ Posts & Telecommun Coll Automat Beijing Peoples R China China Univ Petr East China Coll Informat & Control Engn Qingdao Shandong Peoples R China Qingdao Topscomm Commun Co Qingdao Shandong Peoples R China CNPC East China Design Inst Co Qingdao Shandong Peoples R China

An approximate dynamic programming algorithm based on an actor?critic framework is proposed in this article. This algorithm takes the actor?critic framework as the basic framework, in which the actor and the critic are used to approximate the optimal value function and the control strategy, respectively. At first, the linear basis function approximator is used to approximate the value function. Then the method of basis function construction based on system characteristics is introduced. Furthermore, since the injection concentration of ASP flooding has a fixed interval, the action weighting method is adopted to restrict and approximate the optimal control action. The value function parameter and the two strategy parameters are updated by the gradient descent method. Meanwhile the eligibility trace is introduced to accelerate convergence. Finally, ASP flooding with four injection wells and nine production wells is used to test the effect of the proposed method.

关键词： approximate dynamic programming actor?critic framework ASP flooding action weighting eligibility trace

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Bayesian Exploration for approximate dynamic programming

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OPERATIONS RESEARCH 2019年第1期67卷 198-214页

作者： Ryzhov, Ilya O. Mes, Martijn R. K. Powell, Warren B. van den Berg, Gerald Univ Maryland Robert H Smith Sch Business College Pk MD 20742 USA Univ Maryland Inst Syst Res A James Clark Sch Engn College Pk MD 20742 USA Univ Twente Ind Engn & Business Informat Syst NL-7500 AE Enschede Netherlands Princeton Univ Operat Res & Financial Engn Princeton NJ 08540 USA

approximate dynamic programming (ADP) is a general methodological framework for multistage stochastic optimization problems in transportation, finance, energy, and other domains. We propose a new approach to the exploration/exploitation dilemma in ADP that leverages two important concepts from the optimal learning literature: first, we show how a Bayesian belief structure can be used to express uncertainty about the value function in ADP;second, we develop a new exploration strategy based on the concept of value of information and prove that it systematically explores the state space. An important advantage of our framework is that it can be integrated into both parametric and non-parametric value function approximations, which are widely used in practical implementations of ADP. We evaluate this strategy on a variety of distinct resource allocation problems and demonstrate that, although more computationally intensive, it is highly competitive against other exploration strategies.

关键词： approximate dynamic programming optimal learning Bayesian learning correlated beliefs value of information

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Near-optimal planning using approximate dynamic programming to enhance post-hazard community resilience management

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2019年 181卷 116-126页

作者： Nozhati, Saeed Sarkale, Yugandhar Ellingwood, Bruce Chong, Edwin K. P. Mahmoud, Hussam Colorado State Univ Dept Civil & Environm Engn Ft Collins CO 80523 USA Colorado State Univ Dept Elect & Comp Engn Ft Collins CO 80523 USA Colorado State Univ Dept Math Ft Collins CO 80523 USA

The lack of a comprehensive decision-making approach at the community level is an important problem that warrants immediate attention. Network-level decision-making algorithms need to solve large-scale optimization problems that pose computational challenges. The complexity of the optimization problems increases when various sources of uncertainty are considered. This research introduces a sequential discrete optimization approach, as a decision-making framework at the community level for recovery management. The proposed mathematical approach leverages approximate dynamic programming along with heuristics for the determination of recovery actions. Our methodology overcomes the curse of dimensionality and manages multi-state, large-scale infrastructure systems following disasters. We also provide computational results showing that our methodology not only incorporates recovery policies of responsible public and private entities within the community but also substantially enhances the performance of their underlying strategies with limited resources. The methodology can be implemented efficiently to identify near-optimal recovery decisions following a severe earthquake based on multiple objectives for an electrical power network of a testbed community coarsely modeled after Gilroy, California, United States. The proposed optimization method supports risk-informed community decision makers within chaotic post-hazard circumstances.

关键词： approximate dynamic programming Combinatorial optimization Community resilience Electrical power network Rollout algorithm

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Sub-optimal switching in anti-lock brake systems using approximate dynamic programming

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IET CONTROL THEORY AND APPLICATIONS 2019年第9期13卷 1413-1424页

作者： Sardarmehni, Tohid Heydari, Ali Texas A&M Univ Engn Technol & Ind Distribut College Stn TX 77843 USA Southern Methodist Univ Mech Engn Dallas TX 75205 USA

Optimal scheduling in an anti-lock brake system of ground vehicles is performed through approximate dynamic programming for reducing the stopping distance in severe braking. The proposed optimal scheduler explicitly incorporates the hybrid nature of the anti-lock brake system and provides a feedback solution with a negligible computational burden in control calculation. To this goal, an iterative scheme, called the value iteration algorithm, is used to derive the infinite horizon solution to the underlying Hamilton-Jacobi-Bellman equation. Performance of the proposed method in control of the brake system is illustrated using both linear-in-parameter neural networks and multi-layer perceptrons. Simulation results demonstrate potentials of the method.

关键词： optimal control dynamic programming brakes scheduling feedback braking iterative methods multilayer perceptrons infinite horizon road traffic control vehicle dynamics approximate dynamic programming optimal scheduling anti-lock brake system sub-optimal switching ground vehicles feedback solution value iteration algorithm infinite horizon solution Hamilton-Jacobi-Bellman equation linear-in-parameter neural networks multilayer perceptrons

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Inpatient Overflow: An approximate dynamic programming Approach

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M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT 2019年第4期21卷 894-911页

作者： Dai, J. G. Shi, Pengyi Cornell Univ Sch Operat Res & Informat Engn Ithaca NY 14853 USA Chinese Univ Hong Kong Inst Data & Decis Analyt Shenzhen 518172 Guangdong Peoples R China Purdue Univ Krannert Sch Management W Lafayette IN 47907 USA

Problem definition: Inpatient beds are usually grouped into several wards, and each ward is assigned to serve patients from certain "primary" specialties. However, when a patient waits excessively long before a primary bed becomes available, hospital managers have the option to assign her to a nonprimary bed. although it is undesirable. Deciding when to use such "overflow" is difficult in real time and under uncertainty. Relevance: To aid the decision making, we model hospital inpatient flow as a multiclass, multipool parallel-server queueing system and formulate the overflow decision problem as a discrete-time, infinite-horizon average cost Markov decision process (MDP). The MDP incorporates many realistic and important features such as patient arrival and discharge patterns depending on time of day. Methodology: To overcome the curse-of-dimensionality of this formulated MDP, we resort to approximate dynamic programming (ADP). A critical part in designing an ADP algorithm is to choose appropriate basis functions to approximate the relative value function. Using a novel combination of fluid control and single-pool approximation, we develop analytical forms to approximate the relative value functions at midnight, which then guides the choice of the basis functions for all other times of day. Results: We demonstrate, via numerical experiments in realistic hospital settings, that our proposed ADP algorithm is remarkably effective in finding good overflow policies. These ADP policies can significantly improve system performance over some commonly used overflow strategies-for example, in a baseline scenario, the ADP policy achieves a congestion level similar to that achieved by a complete bed sharing policy, while reduces the overflow proportion by 20%. Managerial implications: We quantify the trade-off between the overflow proportion and congestion from implementing ADP policies under a variety of system conditions and generate useful insights. The plotted efficient fro

关键词： multiclass multipool queueing system inpatient flow management approximate dynamic programming

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