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A q-learning based NSGA-II for dynamic flexible job shop scheduling with limited transportation resources

SWARM AND EVOLUTIONARY COMPUTATION 2024年 90卷

作者： Chen, Rensheng Wu, Bin Wang, Hua Tong, Huagang Yan, Feiyi Nanjing Tech Univ Sch Econ & Management Nanjing 211816 Peoples R China Nanjing Tech Univ Sch Mech & Power Engn Nanjing 211816 Peoples R China

With the widespread adoption of intelligent transportation equipment such as AGVs in the manufacturing field, the flexible job shop scheduling considering limited transportation resources has increasingly attracted attention. However, current research does not consider various dynamic disturbances in real production scenarios, resulting in lower executability of scheduling solutions. To solve this problem, a dynamic flexible job shop scheduling model with limited transportation resources is established, aiming to minimize makespan and total energy consumption. Considering three types of disturbances: job cancellation, machine breakdown, and AGV breakdown, the corresponding event-driven rescheduling strategy is proposed, and a rescheduling instability index is designed to measure the performance of the rescheduling strategy. A q-learning-based NSGA-II algorithm (qNSGA-II) is proposed. By learning the feedback historical search experience, it adaptively selects the appropriate neighborhood structures for local search;and a hybrid initialization strategy tailored to the problem characteristics is designed to improve the optimization performance of the algorithm. Through simulation experiments, the effectiveness of the rescheduling strategies and the superiority of the qNSGA-II algorithm in solving such problems are validated.

关键词： Flexible job shop scheduling problem Limited transportation resources Dynamic scheduling NSGA-II q-learning algorithm

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Resource Allocation for Multi-User Cognitive Radio Systems Using Multi-agent q-learning

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Procedia Computer Science 2012年 10卷 46-53页

作者： Azzouna Ahmed Guezmil Amel Sakly Anis Mtibaa Abdellatif CSR GROUP of Electronic and Microelectronic Laboratory University of Monastir Tunisia National Engineering School of Monastir University of Monastir Tunisia

Cognitive Radio (CR) is a new generation of wireless communication system that enables unlicensed users to exploit underutilized licensed spectrum to optimize the radio spectrum utilization. The resource allocation is difficult to achieve in a dynamic distributed environment, in which CR users take decisions to select a channel without negotiation, and react to the environmental changes. This paper focuses on using a multi-agent reinforcement-learning (MARL), q-learning algorithm, on channels selection decision by secondary users in 2×2 and 3×3 cognitive radio system. Numerical results, obtained with MATLAB, demonstrate that resource allocation is realized without any negotiation between secondary and primary users. In this work, the analogy between the numerical and simulated results is also noted.

关键词： Cognitive Radio Resource Allocation Multi-agent Reinforcement learning q-learning algorithm

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Multi-regional Anti-jamming Communication Scheme Based on Transfer learning and q learning

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2019年第7期13卷 3333-3350页

作者： Han, Chen Niu, Yingtao Army Engn Univ PLA Coll Commun Engn Nanjing Jiangsu Peoples R China Nanjing Telecommun Technol Inst Nanjing Jiangsu Peoples R China

The smart jammer launches jamming attacks which degrade the transmission reliability. In this paper, smart jamming attacks based on the communication probability over different channels is considered, and an anti-jamming q learning algorithm (AqLA) is developed to obtain anti jamming knowledge for the local region. To accelerate the learning process across multiple regions, a multi-regional intelligent anti jamming learning algorithm (MIALA) which utilizes transferred knowledge from neighboring regions is proposed. The MIALA algorithm is evaluated through simulations, and the results show that the it is capable of learning the jamming rules and effectively speed up the learning rate of the whole communication region when the jamming rules are similar in the neighboring regions.

关键词： Anti-jamming Reinforcement learning Transfer learning q-learning algorithm Decision-making

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Intelligent event-based output feedback control with q-learning for unmanned marine vehicle systems

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CONTROL ENGINEERING PRACTICE 2020年 105卷 104616-104616页

作者： Zhang, Dan Ye, ZeHua Chen, PengCheng Wang, qing-Guo Zhejiang Univ Technol Res Ctr Automat & Artificial Intelligence Hangzhou 310023 Zhejiang Peoples R China Beijing Normal Univ BNU Zhuhai BNU HKBU United Int Coll BNU UIC Inst Artificial Intelligence & Future Net Rd JinTong 2000 Zhuhai Guangdong Peoples R China

This paper addresses the Dynamic Output Feedback Control (DOFC) problem for the network-based unmanned marine vehicle (UMV) systems, where the controller is located at the land-based console. The output measurements on the system are transmitted to the land-based console via a communication network. A novel event-based transmission mechanism is devised to alleviate the communication burden, where the threshold of the event function is intelligently updated by a q-learning algorithm. Since the communication delay is inevitably present in the networked controller design, a time-delay system model is established which incorporates both event-based transmission and communication delay. With help of the Lyapunov stability theory and the extended reciprocally convex matrix inequality approach, a sufficient condition is obtained which ensures asymptotic stability of the tracking error system while a prescribed H-infinity disturbance attenuation performance is met. Finally, the simulation study on a benchmark system shows that the proposed controller design not only attenuates the Yaw Velocity (YV) error variation and the Yaw Angle (YA) oscillation, but also reduces the communication load effectively. Compared with the existing results in literature, it is shown that the proposed control algorithm can provide a better tracking performance.

关键词： Unmanned marine vehicle (UMV) Adaptive event-triggering q-learning algorithm Linear matrix inequality Dynamic Output Feedback Control (DOFC)

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Trajectory Optimization and Power Allocation Scheme Based on DRL in Energy Efficient UAV-Aided Communication Networks

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Chinese Journal of Electronics 2022年第3期31卷 397-407页

作者： WANG Chaowei CUI Yuling DENG Danhao WANG Weidong JIANG Fan School of Electronic Engineering Beijing University of Posts and Telecommunications Key Laboratory of Universal Wireless Communications Ministry of Education School of Telecommunications and Information Engineering Xi'an University of Posts and Telecommunications

With flexibility, convenience and mobility, unmanned aerial vehicles(UAVS) can provide wireless communication networks with lower costs, easier deployment, higher network scalability and larger *** paper proposes the deep deterministic policy gradient algorithm to jointly optimize the power allocation and flight trajectory of UAV with constrained effective energy to maximize the downlink throughput to ground users. To validate the proposed algorithm, we compare with the random algorithm, q-learning algorithm and deep q network algorithm. The simulation results show that the proposed algorithm can effectively improve the communication quality and significantly extend the service time of UAV. In addition, the downlink throughput increases with the number of ground users.

关键词： unmanned aerial vehicles remotely operated vehicles trajectory optimization q-learning algorithm power allocation scheme flight trajectory communication quality optimisation mobile robots random algorithm deep q network algorithm learning systems constrained effective energy ground users autonomous aerial vehicles network scalability resource allocation UAVS energy efficient UAV-aided communication networks gradient methods deep deterministic policy gradient algorithm trajectory control radio networks wireless communication networks

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Dynamic Scheduling and Path Planning of Automated Guided Vehicles in Automatic Container Terminal

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IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 2005-2019页

作者： Lijun Yue Houming Fan the Department of Transportation Engineering Dalian Maritime UniversityDalian 116026China

The uninterrupted operation of the quay crane(qC)ensures that the large container ship can depart port within laytime,which effectively reduces the handling cost for the container terminal and ship *** qC waiting caused by automated guided vehicles(AGVs)delay in the uncertain environment can be alleviated by dynamic scheduling optimization.A dynamic scheduling process is introduced in this paper to solve the AGV scheduling and path planning problems,in which the scheduling scheme determines the starting and ending nodes of paths,and the choice of paths between nodes affects the scheduling of subsequent *** work proposes a two-stage mixed integer optimization model to minimize the transportation cost of AGVs under the constraint of laytime.A dynamic optimization algorithm,including the improved rule-based heuristic algorithm and the integration of the Dijkstra algorithm and the q-learning algorithm,is designed to solve the optimal AGV scheduling and path schemes.A new conflict avoidance strategy based on graph theory is also proposed to reduce the probability of path conflicts between *** experiments are conducted to demonstrate the effectiveness of the proposed model and algorithm over existing methods.

关键词： Automated container terminal dynamic scheduling path planning q-learning algorithm rule-based heuristic algorithm

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Reinforcement learning for reactive power control

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IEEE TRANSACTIONS ON POWER SYSTEMS 2004年第3期19卷 1317-1325页

作者： Vlachogiannis, JG Hatziargyriou, ND Inst Educ Technol Informat & Comp Technol Dept Lamia 35100 Greece Natl Tech Univ Athens Dept Elect & Comp Engn GR-10682 Athens Greece

This paper presents a Reinforcement learning (RL) method for network constrained setting of control variables. The RL method formulates the constrained load flow problem as a multistage decision problem. More specifically, the model-free learning algorithm (q-Iearning) learns by experience how to adjust a closed-loop control rule mapping states (load flow solutions) to control actions (offline control settings) by means of reward values. Rewards are chosen to express how well control actions cause satisfaction of operating constraints. The q-learning algorithm is applied to the IEEE 14 busbar and to the IEEE 136 busbar system for constrained reactive power control. The results are compared with those given by the probabilistic constrained load flow based on sensitivity analysis demonstrating the advantages and flexibility of the q-learning algorithm. Computing times with another heuristic method is also compared.

关键词： constrained load flow q-learning algorithm reinforcement learning

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A policy-based Monte Carlo tree search method for container pre-marshalling

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2024年第13期62卷 4776-4792页

作者： Wang, Ziliang Zhou, Chenhao Che, Ada Gao, Jingkun Northwestern Polytech Univ Sch Management Xian Peoples R China Northwest Elect Power Design Inst Co Ltd Xian Peoples R China Northwestern Polytech Univ Sch Management Xian 710072 Peoples R China

The container pre-marshalling problem (CPMP) aims to minimise the number of reshuffling moves, ultimately achieving an optimised stacking arrangement in each bay based on the priority of containers during the non-loading phase. Given the sequential decision nature, we formulated the CPMP as a Markov decision process (MDP) model to account for the specific state and action of the reshuffling process. To address the challenge that the relocated container may trigger a chain effect on the subsequent reshuffling moves, this paper develops an improved policy-based Monte Carlo tree search (P-MCTS) to solve the CPMP, where eight composite reshuffling rules and modified upper confidence bounds are employed in the selection phases, and a well-designed heuristic algorithm is utilised in the simulation phases. Meanwhile, considering the effectiveness of reinforcement learning methods for solving the MDP model, an improved q-learning is proposed as the compared method. Numerical results show that the P-MCTS outperforms all compared methods in scenarios where all containers have different priorities and scenarios where containers can share the same priority.

关键词： Container pre-marshalling problem Monte Carlo tree search Markov decision process q-learning algorithm Automated container terminal

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Long-/Short-Term Preference Based Dynamic Pricing and Manufacturing Service Collaboration Optimization

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2022年第12期18卷 8948-8956页

作者： Zhang, Yongping Cheng, Ying Zheng, Haitao Tao, Fei Beihang Univ Sch Econ & Management Beijing 100191 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China

Manufacturing service (MS) collaboration promotes the social collaboration of distributed enterprises, which makes profits through manufacturing resource sharing on platforms. Therefore, the pricing strategy for MSs will affect the collaboration results, and the satisfaction of enterprises with the platform further. Hence, in this article, a personalized dynamic pricing based MS collaboration optimization method is proposed. First, due to the poor information of the enterprise preference, long-term and short-term preferences of enterprises are estimated based on scarcity, including service features, service quantity, and available time. Then, the personalized pricing method is proposed to adapt to the dynamic collaboration process. And the consumer utility model that considers time decay and price changes is constructed, which reflects the utility characteristics of consumers in the actual collaboration process, such as the utility decrease and deceleration rate increase with time consuming. Finally, q-learning algorithm based MS collaboration optimization verifies the effectiveness and superiority of the method.

关键词： Manufacturing Collaboration Pricing Task analysis Optimization Costs Supply and demand Dynamic pricing method long-term short-term preference manufacturing service (MS) collaboration q-learning algorithm

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Cross-Regional Customized Bus Route Planning Considering Staggered Commuting During the COVID-19

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IEEE ACCESS 2021年 9卷 20208-20222页

作者： Wang, Ange Guan, Hongzhi Wang, Pengfei Peng, Liqun Xue, Yunqiang Beijing Univ Technol Fac Urban Construct Beijing 100124 Peoples R China Beijing Univ Technol Minist Educ Key Lab Urban Secur & Disaster Engn Beijing 100124 Peoples R China Hebei Normal Univ Sci & Technol Coll Urban Construct Qinhuangdao 066004 Hebei Peoples R China East China Jiaotong Univ Sch Transportat & Logist Nanchang 330013 Jiangxi Peoples R China

In order to solve the problem of cross-regional customized bus (CB) route planning during the COVID-19, we develop a CB route planning method based on an improved q-learning algorithm. First, we design a sub-regional route planning approach considering commuters' time windows of pick-up stops and drop-off stops. Second, for the CB route with the optimal social total travel cost, we improve the traditional q-learning algorithm, including state-action pair, reward function and update rule of q value table. Then, a setup method of CB stops is designed and the path impedance function is constructed to obtain the optimal operating path between each of the two stops. Finally, we take three CB lines in Beijing as examples for numerical experiment, the theoretical and numerical results show that (i) compared with the current situation, although the actual operating cost of optimized route increases slightly, it is covered by the reduction of travel cost of passengers and the transmission risk of COVID-19 has also dropped significantly;(ii) the improved q-learning algorithm can solve the problem of data transmission lag effectively and reduce the social total travel cost obviously.

关键词： Planning Heuristic algorithms Windows Automobiles Roads Public transportation COVID-19 Customized bus route planning reinforcement learning q-learning algorithm time window

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