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Collaborative scheduling problem pertaining to launch and recovery operations for carrier aircraft 认领 引用
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作者 GUO Fang HAN Wei +3 位作者 LIU Yujie SU Xichao LIU Jie LI Changjiu 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第1期287-306,共20页
The proliferation of carrier aircraft and the integration of unmanned aerial vehicles(UAVs)on aircraft carriers present new challenges to the automation of launch and recovery operations.This paper investigates a coll... The proliferation of carrier aircraft and the integration of unmanned aerial vehicles(UAVs)on aircraft carriers present new challenges to the automation of launch and recovery operations.This paper investigates a collaborative scheduling problem inherent to the operational processes of carrier aircraft,where launch and recovery tasks are conducted concurrently on the flight deck.The objective is to minimize the cumulative weighted waiting time in the air for recovering aircraft and the cumulative weighted delay time for launching aircraft.To tackle this challenge,a multiple population self-adaptive differential evolution(MPSADE)algorithm is proposed.This method features a self-adaptive parameter updating mechanism that is contingent upon population diversity,an asynchronous updating scheme,an individual migration operator,and a global crossover mechanism.Additionally,comprehensive experiments are conducted to validate the effectiveness of the proposed model and algorithm.Ultimately,a comparative analysis with existing operation modes confirms the enhanced efficiency of the collaborative operation mode. 展开更多
关键词 carrier aircraft collaborative scheduling problem launch recovery multiple population differential evolution
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Collaborative Scheduling Strategy for Computation and Power among Multiple Data Centers Based on New Energy State Recognition 认领 引用
2
作者 Qian Yang Shenglei Du +3 位作者 Boyang Chen Yalu Sun Ding Li Zhiheng Zhang 《Energy Engineering》 EI 2026年第8期346-373,共28页
In recent years,as the core infrastructure of the digital economy,data centers have witnessed increasingly prominent issues of energy consumption and carbon emissions.To achieve the goals of“carbon peak”and“carbon ... In recent years,as the core infrastructure of the digital economy,data centers have witnessed increasingly prominent issues of energy consumption and carbon emissions.To achieve the goals of“carbon peak”and“carbon neutrality”,data centers have gradually introduced new energy power such as wind and photovoltaic power.However,the randomness and volatility of their output pose challenges to efficient absorption.Based on the spatiotemporal complementary characteristics of new energy output in multiple data centers and the spatiotemporal migration capability of computing tasks,this paper proposes a new energy-aware adaptive collaborative scheduling strategy for computation and power.The strategy first constructs a regionally differentiated load model to accurately depict the characteristic differences among the Jiangsu-Zhejiang-Shanghai mixed computing power hub,the Gansu highefficiency computing power base,and the coastal green computing power nodes.Then,a dual-mode scheduling algorithm based on Lyapunov optimization is designed,integrating a prediction-reaction mechanism to achieve dynamic balance between system stability and new energy absorption rate.Furthermore,a V-parameter adaptive adjustment mechanism and a hierarchical fault-tolerant guarantee system are proposed to cope with new energy fluctuations and improve system robustness.Simulation results show that the proposed strategy achieves an average new energy absorption rate of 62.3%and 52.8%in normal weather and severe weather scenarios,respectively.The carbon emission per unit computing power is reduced by 20.9%,and the computing power-electricity efficiency is improved by 9.1%,which is significantly better than the static scheduling strategy.This verifies its effectiveness and practicability in improving new energy utilization,ensuring service quality,and reducing carbon emissions. 展开更多
关键词 Collaborative scheduling for computation and power data centers Lyapunov optimization V-parameter adjustment mechanism wind-solar volatility
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An integrated solution for collaborative scheduling of heterogeneous agricultural machines of different types in harvesting-transportation scenarios 认领 引用 被引量：1
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作者 Ning Wang Zhiwen Jin +5 位作者 Man Zhang Jianxing Xiao Tianhai Wang Qiang Sheng Hao Wang Han Li 《Information Processing in Agriculture》 SCIE EI CSCD 2025年第4期522-538,共17页
Efficient coordination of machinery fleets in regional farmland operations remains a significant challenge due to the lack of scientifically grounded scheduling management strategies,high modeling complexity,and eleva... Efficient coordination of machinery fleets in regional farmland operations remains a significant challenge due to the lack of scientifically grounded scheduling management strategies,high modeling complexity,and elevated operational costs.This study proposed an integrated solution for collaborative scheduling of heterogeneous agricultural machines of different types,aiming to address the collaborative scheduling of harvesters and grain trucks in harvest-transport scenarios.Firstly,an electronic farm map was constructed to facilitate path planning and generate unloading points within plots.The study then developed a collaborative scheduling model involving multiple machines,which incorporated heterogeneous parameters such as harvester harvesting speeds and grain truck hopper capacities.The model aims to minimize the total operational time of the machinery fleet.The scheduling problem was addressed by introducing a hybrid greedy heuristic-based improved genetic algorithm.Simulation and experimental validation were conducted using the electronic map of the Shanghai Qingpu unmanned farm.The results demonstrated that the proposed algorithm outperforms three algorithms in optimizing total operational time.For example,when the number of tasks is 20,the average total operational time is reduced by 32.4 min,an improvement of approximately 11.45%compared to the standard genetic algorithm.Additionally,parameter comparison experiments validate the algorithm’s compatibility with heterogeneous parameter settings,thereby substantiating its efficacy in addressing task allocation problems for heterogeneous machinery.The effectiveness of the proposed method in facilitating efficient collaboration among heterogeneous agricultural machines of different types is demonstrated through a case study on collaborative scheduling in harvest-transport scenarios.The findings validate the feasibility and applicability of the proposed approach in effectively addressing real-world agricultural scheduling challenges. 展开更多
关键词 Heterogeneous agricultural machines Collaborative scheduling Scheduling model Genetic algorithm Electronic map
Human Machine Collaborative Support Scheduling System of Intelligence Information from Multiple Unmanned Aerial Vehicles Based on Eye Tracker 认领 引用 被引量：2
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作者 简立轩 尹栋 +1 位作者 沈林成 牛轶峰 《Journal of Shanghai Jiaotong university(Science)》 EI 2017年第3期322-328,共7页
Many human-machine collaborative support scheduling systems are used to aid human decision making by providing several optimal scheduling algorithms that do not take operator's attention into consideration.However... Many human-machine collaborative support scheduling systems are used to aid human decision making by providing several optimal scheduling algorithms that do not take operator's attention into consideration.However, the current systems should take advantage of the operator's attention to obtain the optimal solution.In this paper, we innovatively propose a human-machine collaborative support scheduling system of intelligence information from multi-UAVs based on eye-tracker. Firstly, the target recognition algorithm is applied to the images from the multiple unmanned aerial vehicles(multi-UAVs) to recognize the targets in the images. Then,the support system utilizes the eye tracker to gain the eye-gaze points which are intended to obtain the focused targets in the images. Finally, the heuristic scheduling algorithms take both the attributes of targets and the operator's attention into consideration to obtain the sequence of the images. As the processing time of the images collected by the multi-UAVs is uncertain, however the upper bounds and lower bounds of the processing time are known before. So the processing time of the images is modeled by the interval processing time. The objective of the scheduling problem is to minimize mean weighted completion time. This paper proposes some new polynomial time heuristic scheduling algorithms which firstly schedule the images including the focused targets. We conduct the scheduling experiments under six different distributions. The results indicate that the proposed algorithm is not sensitive to the different distributions of the processing time and has a negligible computational time. The absolute error of the best performing heuristic solution is only about 1%. Then, we incorporate the best performing heuristic algorithm into the human-machine collaborative support systems to verify the performance of the system. 展开更多
关键词 eye tracker polynomial time heuristics human machine interaction collaborative support scheduling system receding horizon optimization policy
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考虑混合工艺的自动化码头多设备资源协同调度优化模型和算法设计 认领 引用 被引量：2
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作者 初良勇 梁冬 +1 位作者 周于佩 章嘉文 《哈尔滨工程大学学报（英文版）》 CSCD 2024年第2期479-490,共12页
Considering the uncertainty of the speed of horizontal transportation equipment,a cooperative scheduling model of multiple equipment resources in the automated container terminal was constructed to minimize the comple... Considering the uncertainty of the speed of horizontal transportation equipment,a cooperative scheduling model of multiple equipment resources in the automated container terminal was constructed to minimize the completion time,thus improving the loading and unloading efficiencies of automated container terminals.The proposed model integrated the two loading and unloading processes of“double-trolley quay crane+AGV+ARMG”and“single-trolley quay crane+container truck+ARMG”and then designed the simulated annealing particle swarm algorithm to solve the model.By comparing the results of the particle swarm algorithm and genetic algorithm,the algorithm designed in this paper could effectively improve the global and local space search capability of finding the optimal solution.Furthermore,the results showed that the proposed method of collaborative scheduling of multiple equipment resources in automated terminals considering hybrid processes effectively improved the loading and unloading efficiencies of automated container terminals.The findings of this study provide a reference for the improvement of loading and unloading processes as well as coordinated scheduling in automated terminals. 展开更多
关键词 Automated terminal Collaborative scheduling Hybrid process Simulated annealing particle swarm algorithm Uncertainty Scheduling Solutions
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