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Decomposition and approximate dynamic programming approach to optimization of train timetable and skip-stop plan for metro networks

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2023年 157卷

作者： Yuan, Yin Li, Shukai Liu, Ronghui Yang, Lixing Gao, Ziyou Beijing Jiaotong Univ Sch Syst Sci Beijing 100044 Peoples R China Univ Leeds Inst Transport Studies Leeds LS2 9JT England

Carefully coordinating train timetables of different operating lines can help reduce transfer delays, which in turn reduces station crowding and improves overall service quality. This paper explores the optimization to train timetable and skip-stop plans that aims to minimize the total passenger waiting time and station crowding. The problem is formulated as a mixed integer non-linear programming model. To effectively address the complexity of the model, a decomposition and approximate dynamic programming approach is designed to convert the original network-level problem into a series of small-scale subproblems, one for each operating line, to be solved quickly in a distributed manner. The effectiveness and practicability of the model and algorithm are demonstrated on two case networks: a small-scale synthetic network of three metro lines and a real-world network based on Beijing metro. The computational results illustrate that the proposed strategy to generate train timetables and skip-stop plans can effectively reduce passenger waiting time and station crowing. The proposed decomposition and approximate dynamic programming approach is also shown to perform more efficiently than traditional heuristic algorithms, such as genetic algorithm and simulated annealing algorithm for large-scale networks.

关键词： Urban metro networks Timetable optimization Transfer coordination Skip-stop pattern approximate dynamic programming

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Cross-docking based factory logistics unitisation process: An approximate dynamic programming approach

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第1期311卷 112-124页

作者： Lyu, Zhongyuan Huang, George Q. Univ Hong Kong Dept Ind & Mfg Syst Engn Hong Kong Peoples R China Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Peoples R China

The factory logistics increases in importance as more processes and types of components are needed to produce a product. Due to the limited industrial space and stressful competitive environment, manufacturers increasingly outsource logistics-related activities to Third-Party Logistics (3PLs) and Supply hubs. However, the challenges, including unsynchronised decisions with other partners, non-value-added warehousing operations, and excessive space occupation, still burden manufacturers. This research proposes a cross-docking-based factory logistics unitisation process (CD-FLUP) that integrates the cross-docking technique into the assembly line feeding process and uses mobile shelves for component holding and delivery. We formulate the CD-FLUP with an integer program to minimise the total number of shelf units put into storage and the number of deliveries to assembly lines. Based on time decomposition, we propose an approximate dynamic programming (ADP) approach to solve the model efficiently. The time-varying value function approximation (VFA) developed in the proposed ADP approach reduces the approximation parameters and speeds up convergence significantly. The numerical experiment shows that the proposed ADP can generate high-quality solutions far more efficiently than a commercial solver. & COPY;2023 Elsevier B.V. All rights reserved.

关键词： Logistics Unitisation Zero warehousing intelligent manufacturing (ZWIM) approximate dynamic programming Cross-docking

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Policy Iteration Based approximate dynamic programming Toward Autonomous Driving in Constrained dynamic Environment

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第5期24卷 5003-5013页

作者： Lin, Ziyu Ma, Jun Duan, Jingliang Li, Shengbo Eben Ma, Haitong Cheng, Bo Lee, Tong Heng Tsinghua Univ Sch Vehicle & Mobil Beijing 100084 Peoples R China Hong Kong Univ Sci & Technol Guangzhou Robot & Autonomous Syst Thrust Guangzhou Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China Univ Sci & Technol Beijing Sch Mech Engn Beijing 100083 Peoples R China Natl Univ Singapore Dept Elect & Comp Engn Singapore 117583 Singapore

In the area of autonomous driving, it typically brings great difficulty in solving the motion planning problem since the vehicle model is nonlinear and the driving scenarios are complex. Particularly, most of the existing methods cannot be generalized to dynamically changing scenarios with varying surrounding vehicles. To address this problem, this development here investigates the framework of integrated decision and control. As part of the modules, static path planning determines the reference candidates ahead, and then the optimal path-tracking controller realizes the specific autonomous driving task. An innovative and effective constrained finite-horizon approximate dynamic programming (ADP) algorithm is herein presented to generate the desired control policy for effective path tracking. With the generalized policy neural network that maps from the state to the control input, the proposed algorithm preserves the high effectiveness for the motion planning problem towards changing driving environments with varying surrounding vehicles. Moreover, the algorithm attains the noteworthy advantage of alleviating the typically heavy computational loads with the mode of offline training and online execution. As a result of the utilization of multi-layer neural networks in conjunction with the actor-critic framework, the constrained ADP method is capable of handling complex and multidimensional scenarios. Finally, various simulations have been carried out to show that the constrained ADP algorithm is effective.

关键词： Planning Autonomous vehicles Vehicle dynamics Task analysis Heuristic algorithms Approximation algorithms Roads Autonomous driving approximate dynamic programming motion planning constrained optimization reinforcement learning

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Cooperative Ecological Adaptive Cruise Control for Plug-In Hybrid Electric Vehicle Based on approximate dynamic programming

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第3期72卷 3132-3145页

作者： Li, Jie Liu, Yonggang Fotouhi, Abbas Wang, Xiangyu Chen, Zheng Zhang, Yuanjian Li, Liang Chongqing Univ State Key Lab Mech Transmiss Chongqing 400044 Peoples R China Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400044 Peoples R China Cranfield Univ Adv Vehicle Engn Ctr Sch Aerosp Transport & Mfg Cranfield MK43 0AL England Tsinghua Univ State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China Kunming Univ Sci & Technol Fac Transportat Engn Kunming 650500 Peoples R China Loughborough Univ Dept Aeronaut & Automot Engn Loughborough LE11 3TU Leics England

Eco-driving control generates significant energy-saving potential in car-following scenarios. However, the influence of preceding vehicle may impose unnecessary velocity waves and deteriorate fuel economy. In this research, a learning-based method is exploited to achieve satisfied fuel economy for connected plug-in hybrid electric vehicles (PHEVs) with the advantage of vehicle-to-vehicle communication system. A data-driven energy consumption model is leveraged to generate reinforcement signals for approximate dynamic programming (ADP) with the consideration of nonlinear efficiency characteristics of hybrid powertrain system. An advanced ADP scheme is designed for connected PHEVs driving in car-following scenarios. In addition, the cooperative information is incorporated to further improve the fuel economy of the vehicle under the premise of driving safety. The proposed method is mode-free and showcases acceptable computational efficiency as well as adaptability. The simulation results demonstrate that the fuel economy during car-following processes is remarkably improved through cooperative driving information, thereby partially paving the theoretical basis for energy-saving transportation.

关键词： approximate dynamic programming cooperative adaptive cruise control eco-driving plug-in hybrid electric vehicle velocity optimization

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Computationally efficient approximate dynamic programming for multi-site production capacity planning with uncertain demands

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FLEXIBLE SERVICES AND MANUFACTURING JOURNAL 2023年第3期35卷 797-837页

作者： Cheng, Chen-Yang Pourhejazy, Pourya Chen, Tzu-Li Natl Taipei Univ Technol Dept Ind Engn & Management Taipei 10608 Taiwan UiT Arctic Univ Norway Dept Ind Engn Lodve Langsgate 2 N-8514 Narvik Norway

With globalization and rapid technological-economic development accelerating the market dynamics, consumers' demand is becoming more volatile and diverse. In this situation, capacity adjustment as an operational strategic decision plays a major role to ensure supply chain responsiveness while maintaining costs at a reasonable norm. This study contributes to the literature by developing computationally efficient approximate dynamic programming approaches for production capacity planning considering uncertainties and demand interdependence in a multi-factory multi-product supply chain setting. For this purpose, the k-Nearest-Neighbor-based approximate dynamic programming and the Rolling-Horizon-based approximate dynamic programming are developed to enable real-time decision support while ensuring the robustness of the outcomes in stochastic decision environments. Given the market volatilities in the Thin Film Transistor-Liquid Crystal Display industry, a real case from this sector is investigated to evaluate the applicability of the developed approach and provide insights for other industry situations. The developed method is less complex to implement, and numerical experiments showed that it is also computationally more efficient compared to Stochastic dynamic programming.

关键词： Production management Capacity planning approximate dynamic programming Stochastic dynamic programming Optimization

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Technical Note-An approximate dynamic programming Approach to the Incremental Knapsack Problem

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OPERATIONS RESEARCH 2023年第4期71卷 1414-1433页

作者： Aouad, Ali Segev, Danny London Business Sch Dept Management Sci & Operat London NW1 4SA England Tel Aviv Univ Sch Mathemat Sci Dept Stat & Operat Res IL-69978 Tel Aviv Israel

We study the incremental knapsack problem, where one wishes to sequentially pack items into a knapsack whose capacity expands over a finite planning horizon, with the objective of maximizing time-averaged profits. Although various approximation algorithms were developed under mitigating structural assumptions, obtaining nontrivial performance guarantees for this problem in its utmost generality has remained an open question thus far. In this paper, we devise a polynomial-time approximation scheme for general instances of the incremental knapsack problem, which is the strongest guarantee possible given existing hardness results. In contrast to earlier work, our algorithmic approach exploits an approximate dynamic programming formulation. Starting with a simple exponentially sized dynamic program, we prove that an appropriate composition of state pruning ideas yields a polynomially sized state space with negligible loss of optimality. The analysis of this formulation synthesizes various techniques, including new problem decompositions, parsimonious counting arguments, and efficient rounding methods, that may be of broader interest.

关键词： incremental knapsack approximate dynamic programming PTAS

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Decentralized Waveform Co-design for Integrated Sensing and Communications systems via approximate dynamic programming 21

Decentralized Waveform Co-design for Integrated Sensing and ...

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IEEE 21st Consumer Communications and Networking Conference (CCNC)

作者： Doly, Shammi A. Chiriyath, Alex R. Herschfelt, Andrew Azam, Md Ali Ragi, Shankarachary Bliss, Daniel W. Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85287 USA Arizona State Univ Ctr Wireless Informat Syst & Computat Architectur Tempe AZ 85281 USA

ISBN: (纸本)9798350304572

The increasing demand for cost-efficient yet reliable swarms of multi-function unmanned aerial systems (UASs) has tremendous potential in both military and civilian applications. Advancements in ISACs research, such as RF Convergence exclusively provide a promising path toward implementing multi-function UASs, often limited by insufficient front-end capabilities as the network scales up. Because the interconnection bandwidth and computational complexity required to process the data among the ISACs nodes using centralized architectures are high. In this paper, we propose a decentralized waveform co-design method to reduce computational complexity while maximizing the mutual benefits of users in a multi-function UASs network based on the theory of decentralized, partially-observable Markov decision processes (Dec-POMDPs). To address the computational intractability of solving Dec-POMDPs (as with any decision-theoretic framework), we extend an approximate dynamic programming approach we recently developed-nominal belief-state optimization (NBO) in the context of radar-communications waveform co-design. We conduct a numerical study to benchmark the performance of the DecPOMDP-based waveform design approach against a centralized decision optimization approach, which demonstrates a 50% reduction in computation time at the cost of moderate loss in ranging precision.

关键词： RF convergence ISACs Dec-POMDP waveform co-design UASs target tracking approximate dynamic programming

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Quadratic Taylor Expansion-Based approximate dynamic programming for Fully Decentralized AC-OPF of Multi-Area Power Systems

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IEEE TRANSACTIONS ON POWER SYSTEMS 2023年第5期38卷 4940-4949页

作者： Ye, Hanfang Zhu, Jianquan Chen, Jiajun Wang, Zeshuang Zhuo, Yelin Liu, Haixin Liu, Mingbo South China Univ Technol Sch Elect Power Engn Guangzhou 510640 Peoples R China

In this paper, a quadratic Taylor expansion-based approximate dynamic programming (QTE-ADP) algorithm is proposed for the decentralized solution of multi-area AC-optimal power flow (MA-ACOPF). Different from traditional ADP algorithms, the proposed algorithm does not need to approximate the value function via a given function structure, but directly obtains the quadratic Taylor expansion (QTE) of value function based on KKT conditions. Moreover, compared with the commonly used linearized value function approximation techniques, the information used to approximate the value function is also extended from first order to second order in the proposed algorithm, which helps to improve the accuracy and efficiency of ADP. When using QTE-ADP to solve the MA-ACOPF problem, only the boundary voltage information needs to be interchanged between adjacent areas, which facilitates preserving the information privacy and decision independence of each area. Numerical simulations on several test systems demonstrate that the proposed algorithm has good performance in terms of accuracy, efficiency, and adaptability.

关键词： AC-OPF approximate dynamic programming decentralized optimization multi-area power systems quadratic Taylor expansion

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Robust Incremental Learning of approximate dynamic programming for Nonlinear Terminal Guidance

Robust Incremental Learning of Approximate Dynamic Programmi...

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2024 International Conference on Guidance, Navigation and Control

作者： Wang, Han Cheng, Lin Gong, Shengping Beihang Univ Beijing Peoples R China

ISBN: (纸本)9789819622627;9789819622603;9789819622597

Nonlinear optimal guidance problems with terminal constraints are often analytically intractable, and approximate solutions based on reinforcement learning or approximate dynamic programming generally fail to provide stability guarantees due to the presence of inherent approximation errors in neural networks. This paper proposes a robust incremental policy iteration algorithm for nonlinear optimal guidance problems. First, the incremental guidance problem is defined and an incremental policy iteration algorithm is designed to mitigate the initial instability of the classical policy iteration. Then, the boundary of the incremental guidance command is determined by integrating the Lyapunov stability theory into the policy improvement step, which ensures that the entire command is theoretically stable. Simulation results of a specific impact-angle-constrained guidance problem verify advantages of the developed method on efficiency, stability, and optimality.

关键词： optimal guidance approximate dynamic programming neural network Lyapunov stability

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Two-stage robust optimization of power cost minimization problem in gunbarrel natural gas networks by approximate dynamic programming

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Petroleum Science 2022年第5期19卷 2497-2517页

作者： Yi-Ze Meng Ruo-Ran Chen Tian-Hu Deng Department of Industrial Engineering Tsinghua UniversityBeijing100084China

In short-term operation of natural gas network,the impact of demand uncertainty is not *** address this issue we propose a two-stage robust model for power cost minimization problem in gunbarrel natural gas *** demands between pipelines and compressor stations are uncertain with a budget parameter,since it is unlikely that all the uncertain demands reach the maximal deviation *** solving the two-stage robust model we encounter a bilevel problem which is challenging to *** formulate it as a multi-dimensional dynamic programming problem and propose approximate dynamic programming methods to accelerate the *** results based on real network in China show that we obtain a speed gain of 7 times faster in average without compromising optimality compared with original dynamic programming *** results also verify the advantage of robust model compared with deterministic model when facing *** findings offer short-term operation methods for gunbarrel natural gas network management to handle with uncertainties.

关键词： Natural gas Gunbarrel gas pipeline networks Robust optimization approximate dynamic programming

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