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5th International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2024

作者： Wei, Dan Deng, Min Luo, Linghao Ren, Xinshan Xie, Xinyi Liaoning University of Technology Liaoning Jinzhou China Jiangsu Nanjing China

ISBN: (纸本)9798400718007

This article introduces the use of spatiotemporal state networks to handle complex constraints in passenger transportation scheduling. The article also verifies the effectiveness of the branch pricing algorithm and the shortest path model in improving the scheduling efficiency of train attendants, taking Southwest Metro Line 3 and Line 178 as an example. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： scheduling algorithms

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Intelligent scheduling task adjustment method based on graph neural network and adaptive weights 4

Intelligent scheduling task adjustment method based on graph...

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4th International Conference on Computer Vision, Application, and Algorithm, CVAA 2024

作者： Liu, Lei Yu, Xuexia Hu, Zhiyuan Li, Xiaojie Zhao, Guowen Su, Jun Beijing China-Power Information &. Technology Co. Ltd. State Grid Information & Telcommunication Group Beijing100000 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

In order to make the scheduling of tasks to be executed by intelligent agents more flexible and reasonable, a method for adjusting intelligent scheduling tasks based on graph neural networks and adaptive weights is proposed. Based on the short-term memory ability of graph neural networks, search for temporary computing tasks received by intelligent agents in the database as the basic data for subsequent arrangement and adjustment. Extracting thematic content features of intelligent tasks based on recurrent neural networks;Identify priority task targets in the scheduled tasks through graph neural network models. Based on adaptive weights and combined with online learning listwise algorithm, intelligent task scheduling adjustment is achieved. The experimental results show that the conflict rate of task scheduling adjustment using this method remains stable within 5%, and the scheduling adjustment effect is more reasonable;The utilization rate of computing resources is higher after task scheduling adjustment, and task scheduling is more flexible;The longest response time for task scheduling adjustment is 29ms, which is more effective. © 2025 SPIE.

关键词： scheduling algorithms

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Joint Optimization of Intelligent Parking and Automated EV Charging with Advanced Orderly Charging scheduling Algorithm

Joint Optimization of Intelligent Parking and Automated EV C...

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International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Shen, Zhao Wang, Qianqian Lu, Wenchao Peng, Weilun CSG Guangdong Guangzhou Power Supply Bureau Guangzhou China

ISBN: (纸本)9789819639762

Traditional charging stations often suffer from inefficiencies when fully charged vehicles occupy charging infrastructure, reducing resource utilization. In response, we introduce an intelligent, unmanned parking and charging system based on a fully automated robotic arm capable of autonomously managing plug-in and plug-out operations. This enables a single charging unit to serve multiple parking spaces while supporting fully automated charging scheduling. Furthermore, we design an optimized, orderly charging model and algorithm to enhance EV parking slot matching, charging times, frequency, sequence, and power allocation. This approach reduces grid load, increases charging pile utilization, and improves the user experience. Experimental results show that the proposed system achieves a 98.69% charging fulfillment rate compared to 61.6% in conventional systems, along with increased charging profitability. This flexible scheduling and power management solution maximizes charging station efficiency and improves user satisfaction. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： scheduling algorithms

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A New Multiple Agricultural Machinery scheduling via a Genetic Algorithm Improved by Large Neighborhood Search Method 5

A New Multiple Agricultural Machinery Scheduling via a Genet...

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5th IEEE International Conference on Power, Electronics and Computer Applications, ICPECA 2025

作者： Feng, Xin Chen, Xingyu Huang, Lvwen Pu, Pan Northwest A&F University Shaanxi Yangling China

ISBN: (纸本)9798331533694

Aiming at the problem that the commonly used scheduling algorithm in the process of intelligent scheduling of agricultural machinery are easy to fall into local optimum, a multiple agricultural machinery scheduling operation method with a genetic algorithm improved by large neighborhood search method is proposed. To begin with, focusing on the shortcomings of high cost and inefficient of agricultural machinery scheduling, an agricultural machinery scheduling model with a time window is constructed, with the optimization goal of least total operating cost. Subsequently, a genetic algorithm improved by large neighborhood search algorithm is designed. Through selection, crossover, mutation and local search, better solutions are continuously sought. By adding adaptability to the crossover and mutation processes and simultaneously improving the large neighborhood search operator, a more efficient and flexible search for the optimal solution is achieved. Finally, comparative experiments with six commonly used algorithms are carried out based on real data sets. The experimental results show that even if the number of farmlands increases, the scheduling cost of the proposed algorithm still maintains a significant advantage. When the number of farmlands is 17, 27 and 37, the total cost is reduced by an average of 171.40, 305.28 and 460.39 CNY respectively. Moreover, the advantages of this algorithm in terms of convergence results and speed are more prominent. © 2025 IEEE.

关键词： scheduling algorithms

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An improved NSGA-II algorithm for multi-sensor scheduling optimization 4

An improved NSGA-II algorithm for multi-sensor scheduling op...

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4th International Conference on Electronic Information and Communication Engineering, EICE 2025

作者： Yin, Ting Wang, Sheng Zhang, Shusen Institute of System Engineering Academy of Military Science of PLA Beijing China

In this work, we investigate the issue of inadequate solution quality and insufficient convergence in multi-sensor scheduling algorithms for tracking aerial moving targets, and enhance the traditional Non-dominated Sorting Genetic Algorithm II (NSGA-II) through adaptive parameters for crossover and mutation. Taking tracking error and sensor resource consumption into account, we establish two task metrics for a multi-objective optimization problem. The refined algorithm is employed to solve the multi-sensor scheduling optimization problem. Simulation results indicate that the enhanced algorithm achieves superior solution quality compared to the conventional NSGA-II. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： scheduling algorithms

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Optimization of AGV scheduling System Using Genetic Algorithm 14th

Optimization of AGV Scheduling System Using Genetic Algorith...

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14th International Workshop of Advanced Manufacturing and Automation, IWAMA 2024

作者： Zhang, Haitao Zheng, Tianyi Sun, Haoyu Hong, Qi College of Marine Equipment and Mechanical Engineering Jimei University Xiamen361021 China

ISBN: (纸本)9789819626243

This research presents an intelligent AGV scheduling system that optimizes production in manufacturing workshops, enhancing efficiency. It uses real-time monitoring, task allocation, and genetic algorithms to address AGV path planning and task assignment. Results show the model reduces AGV numbers while increasing utilization, optimizing production. This system offers advanced solutions for enterprises aiming for automation and intelligence, helping them gain a competitive edge. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： scheduling algorithms

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Optimized scheduling Model of Integrated Energy System in Industrial Park Based on Improved MPA 14

Optimized Scheduling Model of Integrated Energy System in In...

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14th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Liang, Shuang Jiang, Qingxuan School of Computer Science Northeast Electric Power University Jilin China

ISBN: (纸本)9798331531935

In the context of the global energy shortage and the increasing emphasis on sustainable development goals, optimizing the scheduling of integrated energy systems in industrial parks has become a critical issue. This paper presents an optimal scheduling model and an improved solution method to enhance energy efficiency in industrial parks. Firstly, we develop an energy efficiency optimization model for the integrated energy system in an industrial park. The model aims to maximize system energy efficiency and minimize operational costs while considering load balance constraints, equipment capacity, operational limits, and renewable energy generation constraints. Secondly, we introduce an enhanced Marine Predators Algorithm (MPA) incorporating an adaptive step-size strategy and a multi-subgroup approach. These improvements enable adaptive adjustment of the iteration step size, accelerating convergence and improving solution accuracy, thus facilitating the efficient solving of the model. Then, The effectiveness of the proposed solution method is validated through standard test functions. Finally, we test the model through simulations. The results show that, when considering both energy efficiency and economic factors, our proposed scheduling model effectively improves energy efficiency. It can theoretically reduce energy costs by 3.13% and save 23.683 tons of standard coal annually, all while maintaining the same output level. © 2025 IEEE.

关键词： scheduling algorithms

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Efficient Online Learning Based Cross-Tier Uplink scheduling in HetNets

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IEEE-ACM TRANSACTIONS ON NETWORKING 2022年第6期30卷 2389-2402页

作者： Han, Zhenhua Tan, Haisheng Wang, Rui Hong, Yuncong Lau, Francis C. M. Univ Sci & Technol China USTC Sch Comp Sci & Technol Hefei 230027 Peoples R China Univ Hong Kong HKU Hong Kong Peoples R China USTC Sch Comp Sci & Technol CAS Key Lab Wireless Opt Communicat Hefei 230027 Peoples R China Southern Univ Sci & Technol SUSTech Dept Elect & Elect Engn Shenzhen 518055 Peoples R China Southern Univ Sci & Technol SUSTech Shenzhen 518055 Peoples R China Univ Hong Kong HKU Dept Comp Sci Hong Kong Peoples R China

Heterogeneous cellular networks (HetNets), where low-power low-complexity base stations (Pico-BSs) are deployed inside the coverage of macro base stations (Macro-BSs), can significantly improve the spectrum efficiency by Pico- and Macro base station collaboration. Due to cross-tier interference, joint detection of uplink signals is widely adopted so that Pico-BS can either detect the uplink signals locally or forward them to Macro-BS for processing. The latter can achieve increased throughput at the cost of additional backhaul transmission. In this paper, we study the delay-optimal uplink scheduling problem in HetNets with limited backhaul capacity. Local signal detection or joint signal detection is scheduled in a unified delay-optimal framework. Specifically, we first prove that the problem is NP-hard and then formulate it as a Markov Decision Process. We propose an efficient algorithm, called OLIUS, that can deal with the exponentially growing state and action space. Furthermore, OLIUS is online learning-based which does not require any prior knowledge on user behavior or channel characteristics. We prove the convergence of OLIUS and derive an upper bound on its approximation error. Extensive experiments in various scenarios show our algorithm outperforms existing methods in reducing delay and power consumption.

关键词： Uplink Delays Interference scheduling scheduling algorithms Base stations Throughput Radio communication reinforcement learning scheduling algorithms

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Task scheduling With UAV-Assisted Dispersed Computing for Disaster Scenario

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IEEE SYSTEMS JOURNAL 2022年第4期16卷 6429-6440页

作者： Niu, Zhaocheng Liu, Hui Lin, Xiaomin Du, Junzhao Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China

Unmanned aerial vehicles (UAVs) are beginning to make a splash in emergency disaster scenarios owing to its excellent air mobility and flexibility. Considering that large base stations often cannot be deployed to disaster areas in the first place and the variation of communication links between UAVs, we formulate the task scheduling problem for disaster scenarios as a two-stage Lyapunov optimization problem and propose a dispersed computing network consisting of UAVs and ground mobile devices, which is used for collaborative computing. We decouple the long-term stability of the task queues of the nodes in the system in terms of time slots as a deterministic optimization problem by Lyapunov techniques. By jointly optimizing the task size transmitted from the control center to the UAVs, the task size computed locally and offloaded by the UAVs and mobile devices, the energy consumption of the dispersed computation system is minimized while ensuring the stability of the computation queues. The simulation results verify that our proposed algorithm is close to the optimal case in terms of queue stability, and our algorithm is able to reduce the system energy consumption by more than 50% compared to the local computation of UAVs.

关键词： Task analysis Mobile handsets Energy consumption Computational modeling Power demand Wireless communication Stability criteria Edge computing Internet of Things mobile nodes scheduling algorithms unmanned aerial vehicles (UAVs)

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Multiple Service Model Refreshments in Digital Twin-Empowered Edge Computing

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2024年第5期17卷 2672-2686页

作者： Liang, Xiyuan Liang, Weifa Xu, Zichuan Zhang, Yuncan Jia, Xiaohua Univ Sci & Technol China Sch Comp Sci & Technol Hefeii 230026 Anhui Peoples R China City Univ Hong Kong Dept Comp Sci Kowloon Tong Hong Kong Peoples R China Dalian Univ Technol Sch Software Dalian 116024 Liaoning Peoples R China

Mobile Edge Computing (MEC) has emerged as a promising platform to provide various services for mobile applications at the edge of core networks while meeting stringent service delay requirements of users. Digital twin (DT) that is a mirror of a physical object in cyberspace now becomes a key player in smart cities and the Metaverse, which can be used to simulate or predict the behaviours of the object in future. To enable such a simulation or predication to be more accurate and robust, the state of the digital twin needs to be synchronized (updated) with its object quite often. The quality of inference services in a DT-empowered MEC network usually is determined by the state freshness of service models, while the quality of a service model further is determined by the state freshness of its source DT data. It is vital to refresh the states of service models frequently in order to provide high quality inference services. In this article, we study how to maximize the state freshness of both digital twins and a set of inference service models that are built upon digital twins in an MEC network, while the state freshness of a DT or a service model is achieved through frequent synchronizations between the DT and its physical object. Specifically, we first study a novel cost-aware average model freshness maximization problem with the aim to maximize the average freshness of the states of inference service models while minimizing the cost of achieving the model freshness, and show the NP-hardness of the problem. We then formulate an integer linear programming solution for the offline version of the problem, and devise a performance-guaranteed approximation algorithm for a special case of problem when the monitoring period consists of a single time slot only. Also, we develop an efficient online algorithm for the problem through scheduling objects to upload their update data to their digital twins in the network at each time slot efficiently. We finally evaluate the perfor

关键词： Data models Computational modeling Costs Digital twins Training Predictive models Approximation algorithms minimum-cost generalized assignment problem mobile edge computing (MEC) digital twin state updating scheduling algorithms state refreshment of service models tradeoffs between the state freshness of service models and the update cost

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