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Comparing neuro-dynamic programming algorithms for the vehicle routing problem with stochastic demands

COMPUTERS & OPERATIONS RESEARCH 2000年第11-12期27卷 1201-1225页

作者： Secomandi, N Univ Houston Coll Business Adm Dept Informat & Decis Sci Houston TX 77204 USA

The paper considers a version of the vehicle routing problem where customers' demands are uncertain. The focus is on dynamically routing a single vehicle to serve the demands of a known set of geographically dispersed customers during real-time operations. The goal consists of minimizing the expected distance traveled in order to serve all customers' demands. Since actual demand is revealed upon arrival of the vehicle at the location of each customer, fully exploiting this feature requires a dynamic approach. This work studies the suitability of the emerging field of neuro-dynamic programming (NDP) in providing approximate solutions to this difficult stochastic combinatorial optimization problem. The paper compares the performance of two NDP algorithms: optimistic approximate policy iteration and a rollout policy. While the former improves the performance of a nearest-neighbor policy by 2.3%, the computational results indicate that the rollout policy generates higher quality solutions. The implication for the practitioner is that the rollout policy is a promising candidate for vehicle routing applications where a dynamic approach is required.

关键词： stochastic vehicle routing neuro-dynamic programming rollout policies heuristics

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Minimising total cost with regular and emergency outsourcing sources: a neuro-dynamic programming approach

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2009年第20期47卷 5811-5827页

作者： Dhawan, Aayush Srinivasan, Samashivan Rajib, Prabina Bidanda, Bopaya Univ Pittsburgh Dept Ind Engn Pittsburgh PA 15260 USA Indian Inst Technol Dept Ind Engn & Management Kharagpur 721302 W Bengal India Indian Inst Technol Vinod Gupta Sch Management Kharagpur 721302 W Bengal India

This study models and explains the business situation of an organisation which has regular and emergency outsourcing sources and where decisions have to be made at the beginning of every period regarding how much to order from these sources, so as to balance between different cost components of current and future periods. Previous works in this area have tried to attack this problem with dynamic programming. In this project, neuro-dynamic programming has been applied, and the reasons for doing so have been clearly stated. This model not only derives policies in order to minimise the expected total discounted cost over a period of time, but also enables the system to learn to make such decisions, and to improve its actions by using reinforcement learning. The performance of the present work has been measured quantitatively and has been compared with the models stated in literature. This study will be very useful for the organisations where such business problems exist or are likely to exist. This study will also be of great use to researchers, who are keen to understand and model the given business situation with distribution independent demand based models.

关键词： neuro-dynamic programming emergency outsourcing source learning algorithm

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neuro-dynamic programming method for MPC 1

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IFAC Proceedings Volumes 2001年第25期34卷 143-148页

作者： Jong Min Lee Jay H. Lee School of Chemical Engineering Georgia Institute of Technology Atlanta GA 30332 U.S.A

In this paper, we present how the approach of neuro-dynamic programming(NDP) can be used to combat two important deficiencies of the conventional Model Predictive Control (MPC) formulation, the sometimes exorbitant on-line computational requirement and the inability to consider the evolution of uncertainty in the optimal control calculation. We use a simple Van de Vusse reactor to investigate the feasibility of the proposed approach and identify further research issues.

关键词： neuro-dynamic programming Nonlinear Model Predictive Control dynamic programming Stochastic Optimal Control Dual Control

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Boundary Control of Linear One-Dimensional Parabolic PDE using neuro-dynamic programming

Boundary Control of Linear One-Dimensional Parabolic PDE usi...

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IEEE Conference on Control and Applications (CCA)

作者： Talaei, B. Jagannathan, S. Singler, J. Missouri Univ Sci & Technol Dept Elect & Comp Engn Rolla MO 65409 USA Missouri Univ Sci & Technol Dept Math & Stat Rolla MO 65409 USA

ISBN: (纸本)9781479977871

This paper develops a neuro-dynamic programming (NDP) based near optimal boundary control of distributed parameter systems (DPS) governed by linear one-dimensional parabolic partial differential equations (PDE) under Dirichlet boundary control condition. The structure of the optimal cost functional is defined as an extension of its definition from lumped parameter systems (LPS) but for the infinite dimensional state space. Subsequently, the Hamilton-Jacobi-Bellman (HJB) equation is formulated in the infinite dimensional domain without using any model reduction. Since solving the HJB equation for the exact optimal value functional is burdensome, a radial basis function network (RBF) is subsequently proposed to achieve a computationally feasible solution online and in a forward-in time manner. The optimal value functional is tuned by using conventional adaptive laws such that the HJB equation error is minimized and accordingly the optimal control policy is derived. Ultimate boundedness (UB) of the closed-loop system is verified by using the Lyapunov theory. The performance of proposed controller is successfully verified on an unstable diffusion reaction process.

关键词： Lyapunov methods closed loop systems distributed parameter systems dynamic programming linear systems lumped parameter networks multidimensional systems optimal control partial differential equations radial basis function networks DPS Dirichlet boundary control condition HJB equation Hamilton-Jacobi-Bellman equation LPS Lyapunov theory NDP RBF closed loop system infinite dimensional state space linear one-dimensional parabolic PDE linear one-dimensional parabolic partial differential equations lumped parameter systems model reduction near optimal boundary control neuro-dynamic programming optimal cost functional radial basis function network ultimate boundedness Aerospace electronics Approximation methods Boundary conditions Control design Kernel Mathematical model Optimal control Radial basis function networks Closed loop systems Distributed parameter systems Optimal control dynamic programming lumped parameter systems Lumped parameter networks Reduced order model avionics NDP gene Multidimensional systems digital power system Approximation method Lyapunov methods design control

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Local Tracking Control for Unknown Interconnected Systems via neuro-dynamic programming 25th

Local Tracking Control for Unknown Interconnected Systems vi...

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25th International Conference on Neural Information Processing (ICONIP)

作者： Zhao, Bo Liu, Derong Ha, Mingming Wang, Ding Xu, Yancai Wei, Qinglai Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China

ISBN: (纸本)9783030042394;9783030042387

This paper develops a neuro-dynamic programming based local tracking control (LTC) scheme for unknown interconnected systems. By using the local input-output data and the desired states of coupling subsystems, a local neural network (NN) identifier is established to obtain the local input gain matrix online. By introducing a modified local cost function, the Hamilton-Jacobi-Bellman equation is solved by a local critic NN with asymptotically convergent weight vector, which is obtained by nested update law, and the LTC can be derived with the desired state aided augmented subsystem. The stability of the closed-loop system is shown by Lyapunov's direct method. The simulation on the parallel inverted pendulum system illustrates that the developed LTC scheme is effective.

关键词： Adaptive dynamic programming neuro-dynamic programming Local tracking control Optimal control Unknown interconnected systems

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Joint Routing and Bitrate Adjustment for DASH Video via neuro-dynamic programming in SDN 1

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23rd International Conference on Neural Information Processing (ICONIP)

作者： Zhu, Kunjie Jiang, Junchao Yang, Bowen Cai, Weizhe Yang, Jian Univ Sci & Technol China Hefei Peoples R China

ISBN: (数字)9783319466873

ISBN: (纸本)9783319466873;9783319466866

This paper considers the joint routing and bitrate adjustment optimization for DASH (dynamic Adaptive Streaming over HTTP) video service using neuro-dynamic programming (NDP) in software-defined networking (SDN). We design an open optimization architecture based on OpenFlow based SDN. Following this architecture, we formulate the joint routing and bitrate adjustment problem as a Markov Decision Process (MDP) for maximizing the average reward. In order to solve the curses of dimensionality, we employ neuro-dynamic programming method to conceive an online learning framework and develop a NDP based joint routing and bitrate adjustment algorithm for DASH video service. At last, an emulation platform based on POX and Mininet is constructed to verify the performance of the proposed algorithm. The experimental results indicate our algorithm has more excellent performance compared with OSPF based algorithm.

关键词： DASH neuro-dynamic programming Bitrate adjustment Routing algorithm Software-defined networking

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Constrained Online Optimal Control for Continuous-Time Nonlinear Systems Using neuro-dynamic programming 33

Constrained Online Optimal Control for Continuous-Time Nonli...

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33rd Chinese Control Conference (CCC)

作者： Yang Xiong Liu Derong Wang Ding Ma Hongwen Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (纸本)9781479947249

This paper develops an online adaptive optimal control scheme to solve the infinite-horizon optimal control problem of continuous-time nonlinear systems with control constraints. A novel architecture is presented to approximate the Hamilton-Jacobi-Bellman equation. That is, only a critic neural network is used to derive the optimal control instead of typical actioncritic dual networks employed in neuro-dynamic programming methods. Meanwhile, unlike existing tuning laws for the critic, the newly developed critic update rule not only ensures convergence of the critic to the optimal control but also guarantees the closed-loop system to be uniformly ultimately bounded. In addition, no initial stabilizing control is required. Finally, an example is provided to verify the effectiveness of the present approach.

关键词： Constrained input neuro-dynamic programming Nonlinear systems Online control Optimal control

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Real-Time Controller Design for a Parallel Hybrid Electric Vehicle Using neuro-dynamic programming Method

Real-Time Controller Design for a Parallel Hybrid Electric V...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Boyali, Ali Guvenc, Levent RM Mech Ltd Istanbul Turkey Istanbul Tech Univ Istanbul Turkey

ISBN: (纸本)9781424465880

The use of the neuro-dynamic programming method for real-time control of a parallel hybrid electric vehicle is addressed in this study. The validated model of a research prototype parallel hybrid electric light commercial vehicle, FOHEV I, is used in the numerical parts of this paper. A diesel engine and an electric motor power the front and rear axles, respectively. Due to the computational complexity and resulting burden for optimum power distribution in the hybrid electric powertrain, real-time computation using dynamic programming is not feasible. Sub-optimal optimization techniques are available for pre-defined speed profiles, however, the vehicle speed profile depends on the driver input and vehicle and road conditions and is not known a priori. A neuro-dynamic programming method is proposed here to solve this problem. The results are compared with and found to be quite close to the optimal ones computed using dynamic programming.

关键词： neuro-dynamic programming angular velocity control computational complexity diesel engine diesel engines dynamic programming electric motor power hybrid electric vehicles hybrid electric vehicles neuro-controllers neuro-dynamic programming neurocontrollers optimisation optimization parallel hybrid electric vehicle power distribution power distribution control real time control real-time controller speed control

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Fault tolerant control for a class of nonlinear systems with multiple faults using neuro-dynamic programming

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neuroCOMPUTING 2023年第1期553卷

作者： Zeng, Chujian Zhao, Bo Liu, Derong Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Beijing Normal Univ Sch Syst Sci Beijing 100875 Peoples R China Southern Univ Sci & Technol Sch Syst Design & Intelligent Mfg Shenzhen 518055 Peoples R China Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA

Considering the multiple fault scenario, i.e., both actuator and sensor faults occur simultaneously, this paper develops a neuro-dynamic programming (NDP)-based fault tolerant control (FTC) scheme for a class of nonlinear systems. By combining a descriptor observer with an adaptive observer, system states and multiple faults are estimated simultaneously. For the nominal system, i.e., the fault-free system, a critic neural network (NN) is employed to solve the Hamilton-Jacobi-Bellman (HJB) equation, and the approximate optimal control policy is obtained. To eliminate the influence of sensor faults, the accurate estimations of system states are used instead of system states from faulty sensors to construct the approximate optimal control policy. Then, by combining the estimations of actuator faults with the approximate optimal control policy, an FTC law is developed to suppress the influence of actuator faults. The stability of the closed-loop nonlinear system is analyzed to be uniformly ultimately bounded via the Lyapunov stability theorem. The effectiveness of the present FTC scheme is demonstrated by two simulation examples.

关键词： neuro-dynamic programming Neural networks Fault tolerant control Descriptor observer Multiple faults Adaptive observer

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A SELECTIVE IMPROVEMENT TECHNIQUE FOR FASTENING neuro-dynamic programming IN WATER RESOURCE NETWORK MANAGEMENT

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IFAC Proceedings Volumes 2005年第1期38卷 7-12页

作者： A. Castelletti D. de Rigo A.E. Rizzoli R. Soncini-Sessa E. Weber Dipartimento di Elettronica e Informazione Politecnico di Milano Italy IDSIA Manno Switzerland

An approach to the integrated water resources management based on neuro-dynamic programming (NDP) with an improved technique for fastening its Artificial Neural Network (ANN) training phase will be presented. When dealing with networks of water resources, Stochastic dynamic programming provides an effective solution methodology but suffers from the so-called “curse of dimensionality”, that rapidly leads to the problem intractability. NDP can sensibly mitigate this drawback by approximating the solution with ANNs. However in the real world applications NDP shows to be considerably slowed just by this ANN training phase. To overcome this limit a new training architecture (SIEVE: Selective Improvement by Evolutionary Variance Extinction) has been developed. In this paper this new approach is theoretically introduced and some preliminary results obtained on a real world case study are presented.

关键词： Integrated Water Resources Management Stochastic dynamic programming neuro-dynamic programming Evolutionary algorithm

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