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Deep Reinforcement Learning-based Multi-Objective Scheduling for Distributed Heterogeneous Hybrid Flow Shops with Blocking Constraints

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Engineering 2025年第3期46卷 278-291页

作者： Xueyan Sun Weiming Shen Jiaxin Fan Birgit Vogel-Heuser Fandi Bi Chunjiang Zhang State Key Laboratory of Intelligent Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan 430074China Institute of Automation and Information Systems Technical University of MunichGarching 85748Germany

This paper investigates a distributed heterogeneous hybrid blocking flow-shop scheduling problem(DHHBFSP)designed to minimize the total tardiness and total energy consumption simultaneously,and proposes an improved proximal policy optimization(IPPO)method to make real-time decisions for the DHHBFSP.A multi-objective Markov decision process is modeled for the DHHBFSP,where the reward function is represented by a vector with dynamic weights instead of the common objectiverelated scalar value.A factory agent(FA)is formulated for each factory to select unscheduled jobs and is trained by the proposed IPPO to improve the decision *** FAs work asynchronously to allocate jobs that arrive randomly at the shop.A two-stage training strategy is introduced in the IPPO,which learns from both single-and dual-policy data for better data *** proposed IPPO is tested on randomly generated instances and compared with variants of the basic proximal policy optimization(PPO),dispatch rules,multi-objective metaheuristics,and multi-agent reinforcement learning *** experimental results suggest that the proposed strategies offer significant improvements to the basic PPO,and the proposed IPPO outperforms the state-of-the-art scheduling methods in both convergence and solution quality.

关键词： Multi-objective Markov decision process Multi-agent deep reinforcement learning Proximal policy optimization Distributed hybrid flow-shop scheduling Blocking constraints

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Inexact proximal gradient algorithm with random reshuffling for nonsmooth optimization

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Science China(information Sciences) 2025年第1期68卷 219-237页

作者： Xia JIANG Yanyan FANG Xianlin ZENG Jian SUN Jie CHEN National Key Lab of Autonomous Intelligent Unmanned Systems School of AutomationBeijing Institute of Technology Beijing Institute of Technology Chongqing Innovation Center School of Electronic and Information Engineering Tongji University

Proximal gradient algorithms are popularly implemented to achieve convex optimization with nonsmooth regularization. Obtaining the exact solution of the proximal operator for nonsmooth regularization is challenging because errors exist in the computation of the gradient; consequently, the design and application of inexact proximal gradient algorithms have attracted considerable attention from researchers. This paper proposes computationally efficient basic and inexact proximal gradient descent algorithms with random reshuffling. The proposed stochastic algorithms take randomly reshuffled data to perform successive gradient descents and implement only one proximal operator after all data pass through. We prove the convergence results of the proposed proximal gradient algorithms under the sampling-without-replacement reshuffling *** computational errors exist in gradients and proximal operations, the proposed inexact proximal gradient algorithms can converge to an optimal solution neighborhood. Finally, we apply the proposed algorithms to compressed sensing and compare their efficiency with some popular algorithms.

关键词： proximal operator random reshuffling inexact computation compressed sensing nonsmooth optimization

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Data-driven output regulation control for constrained linear systems

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Science China(information Sciences) 2025年第3期68卷 338-353页

作者： Chaoyu XIA Yi DONG Chaoli WANG Shengyuan XU Shanghai Research Institute for Intelligent Autonomous Systems Tongji University College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous SystemsTongji University Department of Control Science and Engineering School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and Technology Department of Automation Nanjing University of Science and Technology

This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.

关键词： output regulation constrained system data-driven.

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Distributed Momentum-Based Frank-Wolfe Algorithm for Stochastic Optimization

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IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 685-699页

作者： Jie Hou Xianlin Zeng Gang Wang Jian Sun Jie Chen National Key Laboratory of Autonomous Intelligent Unmanned Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China National Key Laboratory of Autonomous Intelligent Unmanned Systems School of AutomationBeijing Institute of TechnologyBeijing 100081 Beijing Institute of Technology Chongqing Innovation Center Chongqing 401120China School of Electronic and Information Engineering Tongji UniversityShanghai 200082and also with the National Key Laboratory of Autonomous Intelligent Unmanned Systemsthe School of AutomationBeijing Institute of TechnologyBeijing 100081China IEEE

This paper considers distributed stochastic optimization,in which a number of agents cooperate to optimize a global objective function through local computations and information exchanges with neighbors over a *** optimization problems are usually tackled by variants of projected stochastic gradient ***,projecting a point onto a feasible set is often *** Frank-Wolfe(FW)method has well-documented merits in handling convex constraints,but existing stochastic FW algorithms are basically developed for centralized *** this context,the present work puts forth a distributed stochastic Frank-Wolfe solver,by judiciously combining Nesterov's momentum and gradient tracking techniques for stochastic convex and nonconvex optimization over *** is shown that the convergence rate of the proposed algorithm is O(k^(-1/2))for convex optimization,and O(1/log_(2)(k))for nonconvex *** efficacy of the algorithm is demonstrated by numerical simulations against a number of competing alternatives.

关键词： Distributed optimization Frank-Wolfe(FW)algorithms momentum-based method stochastic optimization

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Learning Scalable Task Assignment with Imperative-Priori Conflict Resolution in Multi-UAV Adversarial Swarm Defense Problem

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Journal of systems Science & Complexity 2024年第1期37卷 369-388页

作者： ZHAO Zhixin CHEN Jie XIN Bin LI Li JIAO Keming ZHENG Yifan School of Electronics and Information Engineering Tongji UniversityShanghai 201804China Shanghai Institute of Intelligent Science and Technology Tongji UniversityShanghai 201804China School of Automation Beijing Institute of TechnologyBeijing 100081China National Key Laboratory of Autonomous Intelligent Unmanned Systems Beijing 100081China Shanghai Institute of Intelligent Science and Technology Tongji UniversityShanghai 201804China.

The multi-UAV adversary swarm defense(MUASD)problem is to defend a static base against an adversary UAV swarm by a defensive UAV *** the problem into task assignment and low-level interception strategies is a widely used ***-based approaches for task assignment are a promising *** studies on learning-based methods generally assume decentralized decision-making architecture,which is not beneficial for conflict *** contrast,centralized decision-making architecture is beneficial for conflict resolution while it is often detrimental to *** achieve scalability and conflict resolution simultaneously,inspired by a self-attention-based task assignment method for sensor target coverage problem,a scalable centralized assignment method based on self-attention mechanism together with a defender-attacker pairwise observation preprocessing(DAP-SelfAtt)is ***,an imperative-priori conflict resolution(IPCR)mechanism is proposed to achieve conflict-free ***,the IPCR mechanism is parallelized to enable efficient *** validate the algorithm,a variant of proximal policy optimization algorithm(PPO)is employed for training in scenarios of various *** experimental results show that the proposed algorithm not only achieves conflict-free task assignment but also maintains scalability,and significantly improve the success rate of defense.

关键词： Conflict resolution reinforcement learning scalability task assignment.

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A discrete-time looped functional approach and its application to discrete-time systems with cyclically varying delays

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Science China(information Sciences) 2025年

作者： Wen-Hu CHEN Chuan-Ke ZHANG Chen-Rui WANG Ke-You XIE Yong HE Hong-Bing ZENG School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration School of Electrical and Information Engineering Hunan University of Technology

This paper investigates the stability analysis problem of discrete-time systems with cyclically varying *** previous studies, the delay investigated in this paper cyclically alternates between increasing and decreasing changes within its upper and lower bounds. First, to reasonably describe the cyclic variation characteristics of the delay, a discretetime looped functional(DTLF) approach and a refined allowable delay set(ADS) are proposed. Then, based on the constructed DTLF with delay-product terms and using matrix-separation-based inequality to estimate its forward difference,delay-variation-interval-dependent stability conditions considering two different ADSs are derived. Finally, the effectiveness and superiority of the obtained conditions are demonstrated through two numerical examples.

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Automatic velocity picking based on optimal key points tracking algorithm

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Petroleum Science 2024年第2期21卷 903-917页

作者： Yong-Hao Wang Wen-Kai Lu Song-Bai Jin Yang Li Yu-Xuan Li Xiao-Feng Gu The Institute for Artificial Intelligence(THUAI) Tsinghua UniversityBeijing100084China State Key Laboratory of Intelligent Technology and Systems Tsinghua UniversityBeijing100084China Beijing National Research Center for Information Science and Technology(BNRist) Beijing100084China The Department of Automation Tsinghua UniversityBeijing100084China

Picking velocities from semblances manually is laborious and necessitates experience. Although various methods for automatic velocity picking have been developed, there remains a challenge in efficiently incorporating information from nearby gathers to ensure picked velocity aligns with seismic horizons while also improving picking accuracy. The conventional method of velocity picking from a semblance volume is computationally demanding, highlighting a need for a more efficient strategy. In this study, we introduce a novel method for automatic velocity picking based on multi-object tracking. This dynamic tracking process across different semblance panels can integrate information from nearby gathers effectively while maintaining computational efficiency. First, we employ accelerated density clustering on the velocity spectrum to discern cluster centers without the requirement for prior knowledge regarding the number of clusters. These cluster centers embody the maximum likelihood velocities of the main subsurface structures. Second, our proposed method tracks key points within the semblance volume. Kalman filter is adopted to adjust the tracking process, followed by interpolation on these tracked points to construct the final velocity model. Our synthetic data example demonstrates that our proposed algorithm can effectively rectify the picking errors of the clustering algorithm. We further compare the performances of the clustering method(CM), the proposed tracking method(TM), and the variational method(VM) on a field dataset from the Gulf of Mexico. The results attest that our method offers superior accuracy than CM, achieves comparable accuracy with VM, and benefits from a reduced computational cost.

关键词： Velocity picking Multi-object tracking Density clustering Kalman filter

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On large action space in EV charging scheduling optimization

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Science China(information Sciences) 2022年第2期65卷 45-60页

作者： Zhaoyu JIANG Qing-Shan JIA Xiaohong GUAN Center for Intelligent and Networked Systems Beijing National Research Center for Information Science and Technology Department of Automation Tsinghua University MOE KLINNS Lab Xi'an Jiaotong University

In order to reduce the air pollution by traditional fossil fuel and save the charging cost, much attention has been paid to the charging scheduling of electric vehicles(EVs) in the uncertain supplement of wind power and solar power. Owing to the randomness in the renewable power generation and the EV charging demand, simulation-based policy improvement(SBPI) has been well adopted for making charging decision. However, it is challenging to explore the large action space which grows exponentially with respect to the system scale. We consider this important problem in this paper and make the following ***, we explore the structural property of the problem and develop an urgency index to rank the ***, we apply three methods to search in the action space. Third, we numerically demonstrate the performance of the urgency index and the search methods, and compare the SBPI methods with the CPLEXbased method. It shows that SBPI improves the base policies in all these cases.

关键词： EV simulation-based policy improvement large action space sampling

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Experimental implementation of a qubit-efficient variational quantum eigensolver with analog error mitigation on a superconducting quantum processor

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Science China(Physics,Mechanics & Astronomy) 2025年第7期Mechanics & Astronomy) .卷 71-78页

作者： Yuwei Ma Weiting Wang Xianghao Mu Weizhou Cai Ziyue Hua Xiaoxuan Pan Dong-Ling Deng Rebing Wu Chang-Ling Zou Lei Wang Luyan Sun Center for Quantum Information Institute for Interdisciplinary Information SciencesTsinghua University Shanghai Qi Zhi Institute Hefei National Laboratory Center for Intelligent and Networked Systems Department of AutomationTsinghua University CAS Key Laboratory of Quantum Information University of Science and Technology of China Beijing National Laboratory for Condensed Matter Physics and Institute of Physics Chinese Academy of Sciences

We experimentally demonstrate a qubit-efficient variational quantum eigensolver(VQE) algorithm using a superconducting quantum processor,employing minimal quantum resources with only a transmon qubit coupled to a high-coherence photonic *** leveraging matrix product states to compress the quantum state representation,we simulate an N+1-spin circular Ising model with a transverse ***,we develop an analog error mitigation approach through zero-noise extrapolation by introducing a precise noise injection technique for the transmon *** a validation,we apply our error-mitigated qubit-efficient VQE in determining the ground state energies of a 4-spin Ising *** results demonstrate the feasibility of performing quantum algorithms with minimal quantum resources while effectively mitigating the impact of noise,offering a promising pathway to bridge the gap between theoretical advances and practical implementations on current noisy intermediate-scale quantum devices.

关键词： qubit-efficient variational quantum eigensolver (VQE) superconducting quantum processor analog error mitigation

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Multiagent Autonomous Source Search Using Submodularity and Branch-and-Bound

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无人系统（英文） 2024年第1期12卷 19-28页

作者： Xiaoling Xu Damián Marelli Wei Meng Qianqian Cai Minyue Fu School of Automation Guangdong University of TechnologyGuangdong Province Key Laboratory of Intelligent Decision and Cooperative Control Guangzhou 510006P.R.China School of Automation Guangdong University of TechnologyGuangdong Province Key Laboratory of Intelligent Decision and Cooperative Control Guangzhou 510006P.R.China French Argentine International Center for Information and Systems Sciences National Scientific and Technical Research Council Argentina School of Engineering University of Newcastle NSW 2308Australia

This paper is concerned with near-optimal source search problem using a multiagent system in cluttered indoor *** goal of the problem is to maximize the detection probability within the minimum search *** propose a two-stage strategy to achieve this *** the first stage,a greedy approach is used to define a set of grid cells with the aim of maximizing the detection *** the second stage,an iterative branch-and-bound procedure is used to design the search paths of all agents so that all grid cells are visited by one agent and the largest search path among all agents is *** results show that the proposed search algorithm has better performance in terms of exploration time compared to other existing methods.

关键词： Multiagent system source search submodularity branch-and-bound indoor exploration

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