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Swarm-based approximate dynamic optimization process for discrete particle swarm optimization system

INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2009年第1-2期1卷 61-70页

作者： Kang, Qi Wang, Lei Wu, Qidi Tongji Univ Dept Control Sci & Engn Shanghai 201804 Peoples R China

This paper presents a convergence analysis of particle swarm optimization system by treating it as a discrete-time linear time-variant system firstly. And then, based on the results of system convergence conditions, dynamic optimal control of a deterministic PSO system for parameters optimization is studied by using dynamic programming;and an approximate dynamic programming algorithm - swarm-based approximate dynamic programming (swarm-ADP) is proposed in this paper. Finally, numerical simulations proved the validated of this presented dynamic optimization method.

关键词： particle swarm optimization PSO approximate dynamic programming dynamic optimization

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Adaptive critic learning techniques for engine torque and air-fuel ratio control

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 2008年第4期38卷 988-993页

作者： Liu, Derong Javaherian, Hossein Kovalenko, Olesia Huang, Ting Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA Gen Motors Res & Dev Ctr Powertrain Syst Res Lab Warren MI 48090 USA McMaster Carr Elmhurst IL 60126 USA

A new approach for engine calibration and control is proposed. In this paper, we present our research results on the implementation of adaptive critic designs for self-learning control of automotive engines. A class of adaptive critic designs that can be classified as'(model-free) action-dependent heuristic dynamic programming is used in this research project. The goals of the present learning control design for automotive engines include improved performance, reduced emissions, and maintained optimum performance under various operating conditions. Using the data from a test vehicle with a V8 engine, we developed a neural network model of the engine and neural network controllers based on the idea of approximate dynamic programming to achieve optimal control. We have developed and simulated self-learning neural network controllers for both engine torque (TRQ) and exhaust air-fuel ratio (AFR) control. The goal of TRQ control and AFR control is to track the commanded values. For both control problems;excellent neural network controller transient performance has been achieved.

关键词： adaptive critic designs (ACDs) adaptive dynamic programming air-fuel ratio (AFR) control approximate dynamic programming automotive engine control torque control

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Intramarket Optimization for Express Package Carriers

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TRANSPORTATION SCIENCE 2008年第4期42卷 530-545页

作者： Schenk, Luke Klabjan, Diego Univ Illinois Dept Mech & Ind Engn Urbana IL 61801 USA

The flow of packages and documents in collective groups, called splits, of an express package carrier consists of picking up the packages by a courier at customers' locations and bringing them to a station for sorting. Next the splits are transported, either in bulk or containerized conveyances, to a major regional sorting facility called the ramp. In this work we focus on the afternoon and evening operations concerned with stations and the ramp. We deal with the sorting decisions at the stations and the ramp, as well as the transportation decisions among these locations. We model these processes by means of a dynamic program where time periods represent time slices in the afternoon and evening. The resulting myopic problem is a linear mixed-integer program. The overall model is solved by approximate dynamic programming where the value function is approximated by a linear function. Further strategies are developed to speed up the algorithm and decrease the time needed to find feasible solutions. The methodology is tested on several instances from an international express package carrier. Our solutions are substantially better than the current best practice.

关键词： logistics approximate dynamic programming large-scale optimization

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dynamic Multipriority Patient Scheduling for a Diagnostic Resource

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OPERATIONS RESEARCH 2008年第6期56卷 1507-1525页

作者： Patrick, Jonathan Puterman, Martin L. Queyranne, Maurice Univ Ottawa Telfer Sch Management Ottawa ON K1N 6N5 Canada Univ British Columbia Sauder Sch Business Vancouver BC V6T 1Z2 Canada

We present a method to dynamically schedule patients with different priorities to a diagnostic facility in a public health-care setting. Rather than maximizing revenue, the challenge facing the resource manager is to dynamically allocate available capacity to incoming demand to achieve wait-time targets in a cost-effective manner. We model the scheduling process as a Markov decision process. Because the state space is too large for a direct solution, we solve the equivalent linear program through approximate dynamic programming. For a broad range of cost parameter values, we present analytical results that give the form of the optimal linear value function approximation and the resulting policy. We investigate the practical implications and the quality of the policy through simulation.

关键词： health care approximate dynamic programming Markov decision processes patient scheduling linear programming

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Value Function Approximation using Multiple Aggregation for Multiattribute Resource Management

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JOURNAL OF MACHINE LEARNING RESEARCH 2008年 9卷 2079-2111页

作者： George, Abraham Powell, Warren B. Kulkarni, Sanjeev R. Princeton Univ Dept Operat Res & Financial Engn Princeton NJ 08544 USA Princeton Univ Dept Elect Engn Princeton NJ 08544 USA

We consider the problem of estimating the value of a multiattribute resource, where the attributes are categorical or discrete in nature and the number of potential attribute vectors is very large. The problem arises in approximate dynamic programming when we need to estimate the value of a multiattribute resource from estimates based on Monte-Carlo simulation. These problems have been traditionally solved using aggregation, but choosing the right level of aggregation requires resolving the classic tradeoff between aggregation error and sampling error. We propose a method that estimates the value of a resource at different levels of aggregation simultaneously, and then uses a weighted combination of the estimates. Using the optimal weights, which minimizes the variance of the estimate while accounting for correlations between the estimates, is computationally too expensive for practical applications. We have found that a simple inverse variance formula (adjusted for bias), which effectively assumes the estimates are independent, produces near-optimal estimates. We use the setting of two levels of aggregation to explain why this approximation works so well.

关键词： hierarchical statistics approximate dynamic programming mixture models adaptive learning multiattribute resources

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Adaptive critic motion control design of autonomous wheeled mobile robot by dual heuristic programming

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AUTOMATICA 2008年第11期44卷 2716-2723页

作者： Lin, Wei-Song Yang, Ping-Chieh Natl Taiwan Univ Dept Elect Engn Taipei Taiwan

Autonomous wheeled mobile robot (WMR) needs implementing velocity and path tracking control subject to complex dynamical constraints. Conventionally, this control design is obtained by analysis and synthesis or by domain expert to build control rules. This paper presents an adaptive critic motion control design, which enables WMR to autonomously generate the control ability by learning through trials. The design consists of an adaptive critic velocity control loop and a self-learning posture control loop. The neural networks in the velocity neuro-controller (VNC) are corrected with the dual heuristic programming (DHP) adaptive critic method. Designer simply expresses the control objective by specifying the primary utility function then VNC will attempt to fulfill it through incremental optimization. The posture neuro-controller (PNC) learns by approximating the specialized inverse velocity model of WMR so as to map planned positions to suitable velocity commands. Supervised drive supplies variant velocity commands for PNC and VNC to set up their neural weights. During autonomous drive, while PNC halts learning VNC keeps on correcting its neural weights to optimize the control performance. The proposed design is evaluated on an experimental WMR. The results show that the DHP adaptive critic design is a useful base of autonomous control. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Adaptive critic approximate dynamic programming Mobile robot Neural networks

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Relaxations of weakly coupled stochastic dynamic programs

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OPERATIONS RESEARCH 2008年第3期56卷 712-727页

作者： Adelman, Daniel Mersereau, Adam J. Univ Chicago Grad Sch Business Chicago IL 60637 USA Univ N Carolina Kenan Flagler Business Sch Chapel Hill NC 27599 USA

We consider a broad class of stochastic dynamic programming problems that are amenable to relaxation via decomposition. These problems comprise multiple subproblems that are independent of each other except for a collection of coupling constraints on the action space. We fit an additively separable value function approximation using two techniques, namely, Lagrangian relaxation and the linear programming (LP) approach to approximate dynamic programming. We prove various results comparing the relaxations to each other and to the optimal problem value. We also provide a column generation algorithm for solving the LP-based relaxation to any desired optimality tolerance, and we report on numerical experiments on bandit-like problems. Our results provide insight into the complexity versus quality trade-off when choosing which of these relaxations to implement.

关键词： dynamic programming/optimal control approximate dynamic programming Lagrangian optimization discounted infinite horizon linear programming column generation

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Allocation models and heuristics for the outsourcing of repairs for a dynamic warranty population

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MANAGEMENT SCIENCE 2008年第3期54卷 594-607页

作者： Ding, Li Glazbrook, Kevin D. Kirkbride, Christopher Univ Durham Durham Business Sch Durham DH1 3LB England Univ Lancaster Dept Management Sci Lancaster LA1 4YX England

W e consider a scenario in which a large equipment manufacturer wishes to outsource the work involved in repairing purchased goods while under warranty. Several external service vendors are available for this work. We develop models and analyses to support decisions concerning how responsibility for the warranty population should be divided between them. These also allow the manufacturer to resolve related questions concerning, for example, whether the service capacities of the contracted vendors are sufficient to deliver an effective post-sales service. Static allocation models yield information concerning the proportions of the warranty population for which the vendors should be responsible overall. dynamic allocation models enable consideration of how such overall workloads might be delivered to the vendors over time in a way which avoids excessive variability in the repair burden. We apply dynamic programming policy improvement to develop an effective dynamic allocation heuristic. This is evaluated numerically and is also used as a yardstick to assess two simple allocation heuristics suggested by static models. A dynamic greedy allocation heuristic is found to perform well. Dividing the workload equally among vendors with different service capacities can lead to serious losses.

关键词： approximate dynamic programming greedy heuristics index policies outsourcing warranty repairs

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Value function-based approach to the scheduling of multiple controllers

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JOURNAL OF PROCESS CONTROL 2008年第6期18卷 533-542页

作者： Lee, Jong Min Lee, Jay H. Georgia Inst Technol Sch Chem & Biomol Engn Atlanta GA 30332 USA Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 2G6 Canada

Both gain scheduling and multiple model based control approaches are considered to be practical approaches for control of industrial nonlinear processes. However, the former ignores system dynamics and the latter is specific to the type of controller design and limited in its scope of application as practiced in industry. This paper proposes a value function-based strategy for switching among local controllers, thereby providing an effective global control policy for the entire operating regions. The suggested method selects the best one among a set of available control policies at each time step by evaluating the "value" function associated with the successive state when a particular control action instructed by a candidate policy is taken for a give state. The value function, which maps a state to its associated discounted infinite horizon cost-to-go, is obtained by solving the dynamic programming in an approximate way using closed-loop simulation or operational data and a function approximator. The proposed approach has the advantages that candidate controllers are general and the switching is performed not by a fixed heuristic rule but rigorously via dynamic programming. From the viewpoint of dynamic programming, the approach helps alleviate the curse of dimensionality with respect to the state space and action space. Optimal or approximately optimal switching rules can be learned without a model, which defines the state transitional rule. The approach is demonstrated on several different nonlinear control examples. (C) 2007 Elsevier Ltd. All rights reserved.

关键词： approximate dynamic programming nonlinear control multiple controllers

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DynaCAS: Computational Experiments and Decision Support for ITS

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IEEE INTELLIGENT SYSTEMS 2008年第6期23卷 19-23页

作者： Zhang, Nan Wang, Fei-Yue Zhu, Fenghua Zhao, Dongbin Tang, Shuming Chinese Acad Sci Key Lab Complex Syst & Intelligence Sci Beijing 100864 Peoples R China Chinese Acad Sci Lab Complex Syst & Intelligence Sci Inst Automat Beijing 100864 Peoples R China Shandong Univ Sci & Technol Jinan Peoples R China

Accurate, reliable, and timely traffic information is critical for deployment and operation of intelligent transportation systems (ITSs). Traffic forecasting for travelers and traffic operators should become at least as useful and convenient as weather reports. In the US, the Federal Highway Administration (FHWA) has envisioned a real-time traffic estimation and prediction system (TrEPS) as an ITS Support platform that resides at traffic management centers (TMCs) for dynamic route assignment (DRA) and other transportation operations. To enable ITS deployment for urban traffic control and management in China, in 1999 the Chinese Academy of Sciences Outlined a research agenda to develop related intelligent systems and technology.(1) A central component of this agenda was a REPS called DynaCAS (dynamic traffic assignment based on complex adaptive systems). Here, we briefly introduce DynaCAS and its open source counterpart DynaChina, emphasizing how they differ from other TrEPS projects.

关键词： Traffic Estimation And Prediction System Tr EPS dynamic Route Assignment dynamic Traffic Assignment Computational Experiments approximate dynamic programming

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