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Hybrid Flow Shop Rescheduling Approach Based on Hybrid-Driven Mechanism and Improved Multi-Objective WOA 认领 引用
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作者 Feng Lv Xin Xu +1 位作者 Cheng Yang Yixuan Tang 《Computers, Materials & Continua》 SCIE EI 2026年第7期1982-2009,共28页
To ensure an effective disturbance response and maintain continuous production in hybrid flow shops,this paper focuses on the design of a rescheduling method.A rescheduling model is constructed that minimizes the make... To ensure an effective disturbance response and maintain continuous production in hybrid flow shops,this paper focuses on the design of a rescheduling method.A rescheduling model is constructed that minimizes the makespan,total tardiness,and scheme deviation degree.A hybrid rescheduling driving mechanism based on the latest completion time is designed to effectively trigger rescheduling.The Whale Optimization Algorithm(WOA)is improved by integrating the good point set theory,nonlinear control parameter strategy,and Differential Evolution(DE)algorithm.Moreover,non-dominated sorting and a dynamic external archive mechanism based on crowding distance are introduced to make it suitable for multi-objective optimization problems.The superiority of the Improved Multi-objective Whale Optimization Algorithm(IMOWOA)and the effectiveness of the improved mechanisms are verified through comparative experiments and ablation experiments.Taking the final assembly production line of an agricultural machinery equipment enterprise as an example,a rescheduling scheme is generated based on the practical production requirements,which verifies the feasibility and effectiveness of the proposed method. 展开更多
关键词 Hybrid flow shop production disturbance production rescheduling rescheduling driving mechanism improved multi-objective whale optimization algorithm
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MDMOSA:Multi-Objective-Oriented Dwarf Mongoose Optimization for Cloud Task Scheduling 认领 引用
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作者 Olanrewaju Lawrence Abraham Md Asri Ngadi +1 位作者 Johan Bin Mohamad Sharif Mohd Kufaisal Mohd Sidik 《Computers, Materials & Continua》 SCIE EI 2026年第3期2062-2096,共35页
Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.Howev... Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.However,traditional approaches frequently rely on single-objective optimization methods which are insufficient for capturing the complexity of such problems.To address this limitation,we introduce MDMOSA(Multi-objective Dwarf Mongoose Optimization with Simulated Annealing),a hybrid that integrates multi-objective optimization for efficient task scheduling in Infrastructure-as-a-Service(IaaS)cloud environments.MDMOSA harmonizes the exploration capabilities of the biologically inspired Dwarf Mongoose Optimization(DMO)with the exploitation strengths of Simulated Annealing(SA),achieving a balanced search process.The algorithm aims to optimize task allocation by reducing makespan and financial cost while improving system resource utilization.We evaluate MDMOSA through extensive simulations using the real-world Google Cloud Jobs(GoCJ)dataset within the CloudSim environment.Comparative analysis against benchmarked algorithms such as SMOACO,MOTSGWO,and MFPAGWO reveals that MDMOSA consistently achieves superior performance in terms of scheduling efficiency,cost-effectiveness,and scalability.These results confirm the potential of MDMOSA as a robust and adaptable solution for resource scheduling in dynamic and heterogeneous cloud computing infrastructures. 展开更多
关键词 Cloud computing multi-objective task scheduling dwarf mongoose optimization metaheuristic
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Multi-Objective Enhanced Cheetah Optimizer for Joint Optimization of Computation Offloading and Task Scheduling in Fog Computing 认领 引用
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作者 Ahmad Zia Nazia Azim +5 位作者 Bekarystankyzy Akbayan Khalid J.Alzahrani Ateeq Ur Rehman Faheem Ullah Khan Nouf Al-Kahtani Hend Khalid Alkahtani 《Computers, Materials & Continua》 SCIE EI 2026年第3期1559-1588,共30页
The cloud-fog computing paradigm has emerged as a novel hybrid computing model that integrates computational resources at both fog nodes and cloud servers to address the challenges posed by dynamic and heterogeneous c... The cloud-fog computing paradigm has emerged as a novel hybrid computing model that integrates computational resources at both fog nodes and cloud servers to address the challenges posed by dynamic and heterogeneous computing networks.Finding an optimal computational resource for task offloading and then executing efficiently is a critical issue to achieve a trade-off between energy consumption and transmission delay.In this network,the task processed at fog nodes reduces transmission delay.Still,it increases energy consumption,while routing tasks to the cloud server saves energy at the cost of higher communication delay.Moreover,the order in which offloaded tasks are executed affects the system’s efficiency.For instance,executing lower-priority tasks before higher-priority jobs can disturb the reliability and stability of the system.Therefore,an efficient strategy of optimal computation offloading and task scheduling is required for operational efficacy.In this paper,we introduced a multi-objective and enhanced version of Cheeta Optimizer(CO),namely(MoECO),to jointly optimize the computation offloading and task scheduling in cloud-fog networks to minimize two competing objectives,i.e.,energy consumption and communication delay.MoECO first assigns tasks to the optimal computational nodes and then the allocated tasks are scheduled for processing based on the task priority.The mathematical modelling of CO needs improvement in computation time and convergence speed.Therefore,MoECO is proposed to increase the search capability of agents by controlling the search strategy based on a leader’s location.The adaptive step length operator is adjusted to diversify the solution and thus improves the exploration phase,i.e.,global search strategy.Consequently,this prevents the algorithm from getting trapped in the local optimal solution.Moreover,the interaction factor during the exploitation phase is also adjusted based on the location of the prey instead of the adjacent Cheetah.This increases the exploitation capability of agents,i.e.,local search capability.Furthermore,MoECO employs a multi-objective Pareto-optimal front to simultaneously minimize designated objectives.Comprehensive simulations in MATLAB demonstrate that the proposed algorithm obtains multiple solutions via a Pareto-optimal front and achieves an efficient trade-off between optimization objectives compared to baseline methods. 展开更多
关键词 Computation offloading task scheduling cheetah optimizer fog computing optimization resource allocation internet of things
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Integrated scheduling optimization of carrier-based aircraft deck operation for comprehensive efficiency 认领 引用 被引量：1
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作者 Wei Han Haonan Wu +2 位作者 Fang Guo Liangliang Cheng Xichao Su 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2026年第8期190-215,共26页
An aircraft carrier's formidable combat capability relies on sorties,with efficient deck operations as the core to enhance sortie and recovery capabilities.First,this study develops an integrated mathematical mode... An aircraft carrier's formidable combat capability relies on sorties,with efficient deck operations as the core to enhance sortie and recovery capabilities.First,this study develops an integrated mathematical model for carrier-based aircraft deployment,sortie,and maintenance scheduling to assist decision-makers in formulating sorties and coordinating resource allocation(e.g.,personnel,equipment),aiming to optimize the comprehensive effectiveness index of integrated carrier-based aircraft scheduling.Second,a hybrid multi-layer coded genetic algorithm(HMCGA)integrated with heuristic rules is proposed;it uses four-layer coding to resolve inter-sub-process coupling and supports integrated carrier-based aircraft scheduling,covering hangar transfer,deck transfer,aircraft maintenance support,and sortie execution.Then,case simulation shows the proposed model and algorithm effectively improve operational effectiveness and ensure accuracy.Finally,comparisons of 50 independent simulation results(PSO,DE,WOA,CPLEX)effectively validate HMCGA's superiority and universality in solving integrated carrier-based aircraft scheduling problems across fleet scales,further confirming the model and algorithm's reliability. 展开更多
关键词 Comprehensive efficiency Hybrid multi-layer coding genetic algorithm Scheduling optimization Integrated scheduling
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Cost-Optimal Building Energy System Scheduling Integrating Solar Irradiance Forecasting via LSTM-Attention-TCN Model 认领 引用 被引量：1
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作者 Zhengtian Wu Jianyu Li +7 位作者 Yang Gao Chuangyin Dang Chao Tang Yuansheng Li Xinmiao Wang Jinpeng Chen Hongbo Gao Xinyin Xu 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第3期695-708,共14页
Building energy systems integrating multiple energy sources can effectively reduce energy consumption and facilitate renewable energy integration.Integrating electrical energy storage(EES)into these systems helps acco... Building energy systems integrating multiple energy sources can effectively reduce energy consumption and facilitate renewable energy integration.Integrating electrical energy storage(EES)into these systems helps accommodate the increasing share of renewables;however,the stochastic and intermittent nature of solar power still poses challenges to supply reliability.This study proposes a photovoltaic(PV)‐oriented storage scheduling strategy,in which short‐term PV generation forecasts are applied to guide the operation of a building power supply network consisting of photovoltaic panels,the grid,and energy storage systems.The forecasting approach employs a hybrid framework combining a Long Short‐Term Memory(LSTM)network to capture temporal dependencies,an attention mechanism to emphasise critical time steps,and a Temporal Convolutional Network(TCN)to map the enhanced features to PV outputs.Experimental evaluation using historical datasets under multiple weather conditions and time periods shows that the proposed LSTM‐Attention‐TCN model achieves a mean absolute error(MAE)of 20.45 W/m2 and a Nash–Sutcliffe efficiency(NSE)of 0.94,outperforming both standalone LSTM and TCN models as well as their hybrid variants in terms of accuracy and robustness.By providing high‐accuracy solar irradiance forecasts to guide energy storage operation and grid interaction,the proposed model enables more efficient and economical scheduling of building energy systems.Compared with an uncontrolled scenario,the LSTM‐Attention‐TCN‐based scheduling reduces the total operating cost by approximately 52.1%,and achieves an additional 16.5%reduction compared to a conventional strategy without predictive coordination.In addition,compared to other hybrid forecasting models such as LSTM‐TCN and TCN‐Attention,the proposed model achieves the lowest total cost of CNY 14.83 and demonstrates superior scheduling efficiency,thereby enhancing the stability and flexibility of building energy utilization. 展开更多
关键词 cost optimization deep learning energy storage systems optimal scheduling solar irradiance forecasting
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Research on Dynamic Scheduling Method for Hybrid Flow Shop Order Disturbance Based on IMOGWO Algorithm 认领 引用
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作者 Feng Lv Huili Chu +1 位作者 Cheng Yang Jiajie Zhang 《Computers, Materials & Continua》 SCIE EI 2026年第3期1199-1221,共23页
To address the issue that hybrid flow shop production struggles to handle order disturbance events,a dynamic scheduling model was constructed.The model takes minimizing the maximum makespan,delivery time deviation,and... To address the issue that hybrid flow shop production struggles to handle order disturbance events,a dynamic scheduling model was constructed.The model takes minimizing the maximum makespan,delivery time deviation,and scheme deviation degree as the optimization objectives.An adaptive dynamic scheduling strategy based on the degree of order disturbance is proposed.An improved multi-objective Grey Wolf(IMOGWO)optimization algorithm is designed by combining the“job-machine”two-layer encoding strategy,the timing-driven two-stage decoding strategy,the opposition-based learning initialization population strategy,the POX crossover strategy,the dualoperation dynamic mutation strategy,and the variable neighborhood search strategy for problem solving.A variety of test cases with different scales were designed,and ablation experiments were conducted to verify the effectiveness of the improved strategies.The results show that each improved strategy can effectively enhance the performance of the IMOGWO.Additionally,performance analysis was conducted by comparing the proposed algorithm with three mature and classical algorithms.The results demonstrate that the proposed algorithm exhibits superior performance in solving the hybrid flow-shop scheduling problem(HFSP).Case validations were conducted for different types of order disturbance scenarios.The results demonstrate that the proposed adaptive dynamic scheduling strategy and the IMOGWO algorithm can effectively address order disturbance events.They enable rapid response to order disturbance while ensuring the stability of the production system. 展开更多
关键词 Hybrid flow shop order disturbance dynamic scheduling improved multi-objective Grey Wolf optimization
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Deep Reinforcement Learning-based Multi-Objective Scheduling for Distributed Heterogeneous Hybrid Flow Shops with Blocking Constraints 认领 引用 被引量：3
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作者 Xueyan Sun Weiming Shen +3 位作者 Jiaxin Fan Birgit Vogel-Heuser Fandi Bi Chunjiang Zhang 《Engineering》 SCIE EI CSCD 2025年第3期278-291,共14页
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 pr... 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 quality.Multiple 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 utilization.The 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 methods.Extensive 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. 展开更多
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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LLM‑assisted adaptive large neighborhood search for agile earth observation satellite scheduling 认领 引用
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作者 Feiran WANG Jiawei CHEN +4 位作者 Yonghao DU Yanjie SONG Yingwu CHEN Rammohan MALLIPEDDI Witold PEDRYCZ 《ENGINEERING Management》 CSCD 2026年第1期213-239,共27页
The Agile Earth Observation Satellite Scheduling Problem(AEOSSP)is a complex NP‑hard challenge that involves selecting,sequencing,and timing observation tasks to maximize imaging profits while adhering to various cons... The Agile Earth Observation Satellite Scheduling Problem(AEOSSP)is a complex NP‑hard challenge that involves selecting,sequencing,and timing observation tasks to maximize imaging profits while adhering to various constraints.In our study,we developed a mixed‑integer programming model for AEOSSP,incorporating key constraints related to visible time windows and time dependencies.To tackle this,we propose an Evolutionary Adaptive Large Neighborhood Search Algorithm(evALNS)enhanced by Large Language Models(LLMs).Our work pioneers the application of LLMs to ALNS by being the first to automatically develop and evolve its critical destroy heuristics.However,a naive application of LLMs is insufficient for such a complex domain.We therefore introduce a novel Dual‑Population Co‑Evolutionary Computing Framework(DPEC)to bridge the LLM’s knowledge gap by synergizing LLM‑generated heuristics with expert‑designed ones.This co‑evolution,guided by a Functional Natural Language Embedding(FNLE)strategy and customized prompts,significantly enhances the adaptability and efficiency of ALNS.Extensive numerical experiments demonstrated the superiority of the evALNS evolved under our framework,achieving an average profit improvement of 8.48%compared to the original ALNS with expert‑designed destroy operators. 展开更多
关键词 large language model algorithm design adaptive large neighborhood search satellite scheduling
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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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Green scheduling for LLM workloads with model and data reuse across geo-distributed data centers 认领 引用
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作者 Hao Liu Xiaonyu Hu +3 位作者 Ran Wang Jie Hao Qiang Wu Hongke Zhang 《Digital Communications and Networks》 SCIE EI CSCD 2026年第2期236-251,共16页
The explosive proliferation of Large Language Models(LLMs)imposes significant energy and operational burdens on Geographically Distributed Data Centers(GDDCs),thereby demanding an efficient mechanism for LLMs task sch... The explosive proliferation of Large Language Models(LLMs)imposes significant energy and operational burdens on Geographically Distributed Data Centers(GDDCs),thereby demanding an efficient mechanism for LLMs task scheduling.While prior geo-distributed scheduling methods reduce cost and carbon emissions by exploiting regional heterogeneity,they largely overlook model and data reuse opportunities and the uncertainty of LLM execution times.In this paper,we introduce GCOS,to the best of our knowledge,the first green scheduling framework that incorporates a dual-cache system for both data and models,while jointly optimizing task assignment and cache migration.We firstly propose a dual-cache mechanism that decouples model and data caching to enable fine-grained reuse and minimize redundant transmissions.Subsequently,we propose the Multi-Agent Cache-aware Cooperative Scheduling(MACCS)algorithm,which leverages reinforcement learning to optimize task placement with a focus on minimizing both carbon emissions and cost.Additionally,we design a lightweight execution time predictor,DiPTree,to address the high variability in task execution times.Extensive experiments on real-world datasets demonstrate that GCOS reduces overall cost by up to 92.6%and carbon emissions by 90.3%,significantly outperforming existing baselines. 展开更多
关键词 Large language model Geographically distributed data center Green communication Task scheduling Multi-agent reinforcement learning
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Efficient user scheduling in mm Wave networks:leveraging knowledge transfer with channel knowledge map 认领 引用
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作者 Chunlong He Peihong He Xingquan Li 《Digital Communications and Networks》 SCIE EI CSCD 2026年第2期319-331,共13页
This paper proposes a Deep Reinforcement Learning(DRL)algorithm for user scheduling in Millimeter Wave(mmWave)networks,which utilizes Channel Knowledge Map(CKM)for knowledge transfer to enhance the learning of schedul... This paper proposes a Deep Reinforcement Learning(DRL)algorithm for user scheduling in Millimeter Wave(mmWave)networks,which utilizes Channel Knowledge Map(CKM)for knowledge transfer to enhance the learning of scheduling strategies.The user scheduling and link configuration problems are modeled as a multiqueue system.Each queue represents the data demand of an individual user.This setup allows the base station to make dynamic scheduling decisions based on changing environmental conditions.This approach facilitates efficient management of user-specific requirements while addressing the challenges posed by dynamic network environments.Our model incorporates relay selection,codebook selection,and beam tracking to support flexible and efficient resource allocation.In contrast to traditional channel model-based optimization,we design algorithms for scheduling policy pre-training using CKMs,which provide information about the channel between specific pairs of locations.Specifically,we assume that the CKM is fully available to allow the complex scheduling network to have a better starting point or follow a more favorable gradient direction through knowledge migration.This integration of CKM with knowledge transfer significantly accelerates DRL convergence and enhances performance stability.Simulation results confirmed the effectiveness of the proposed approach.Relative to the baseline methods,integrating CKM with knowledge transfer accelerated the convergence of the DRL algorithm by approximately 20%,maintained the delay within 30 milliseconds,and reduced the average queue length by nearly 30%. 展开更多
关键词 Millimeter wave User scheduling Knowledge transfer Channel knowledge map Deep reinforcement learning
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Optimization and Scheduling Method for Wind-Solar-Thermal-Storage Power System of Multiple Energy Stations Using Correlation-IGDT 认领 引用
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作者 Yang Liu Yinguo Yang +4 位作者 Pingping Xie Qiuyu Lu Yue Chen Zhanpeng Xu Zejie Huang 《Energy Engineering》 EI 2026年第5期154-170,共17页
With the large-scale integration of wind and solar energy into the power grid,the power system is facing uncertainty challenges in multiple links,such as source,grid,and load.How to efficiently dispatch flexible resou... With the large-scale integration of wind and solar energy into the power grid,the power system is facing uncertainty challenges in multiple links,such as source,grid,and load.How to efficiently dispatch flexible resources,such as energy storage,has become an urgent problem to be solved.To this end,this paper considers the correlation between new energy stations due to natural conditions,uses Vine-Copula theory to describe the correlation characteristics of the output of multiple new energy stations,and proposes a wind solar new energy output scenario generation method based on Vine-Copula theory;Then,to develop the optimal scheduling and operation plan,considering the goal of minimizing operating costs within a scheduling cycle,combined with the scenario of output of wind and solar energy,an optimization and scheduling model for wind-solar-thermal-storage power system operation of multiple energy stations was constructed;On this basis,considering the difficulty in obtaining the probability distribution of load uncertainty,a risk-averse model and a risk-seeking model based on information Gap Decision Theory(IGDT)were constructed,and a multi energy station power system operation optimization scheduling method based on correlation-IGDT was proposed.By setting risk strategies and risk deviation factors,the power system operation scheduling scheme under this strategy can be obtained.Simulation experiments were conducted based on an improved IEEE39 node system for verification,and the results showed that compared to traditional methods that do not consider correlation,this method can reduce thermal power costs by 0.63%and energy storage costs by 10.56%.Meanwhile,Monte Carlo sampling analysis shows that the model has good accuracy and stability within the range of load disturbances.Further analysis shows that under the risk avoidance strategy,the maximum power variation of thermal power is controlled at 284 MW,with an average of 172 MW;while under the risk acceptance strategy,the maximum variation is 198 MW,with an average of 127 MW,significantly improving the system’s adaptability and operational efficiency to uncertain environments.The main contribution of this article is to integrate the modeling of new energy correlation with information gap decision-making and construct a power system scheduling optimization framework for multiple uncertain factors,which has good promotion value and practical application potential. 展开更多
关键词 Vine copula information gap decision theory(IGDT) wind and solar energy optimal scheduling
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Research on unmanned swarm scheduling strategies for mountain obstacle-breaching missions 认领 引用
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作者 WANG Kaisheng HUANG Yanyan +1 位作者 TAN Jinxi ZHAI Wenjie 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第1期26-35,共10页
In response to the challenges faced by unmanned swarms in mountain obstacle-breaching missions within complex terrains,such as poor task-resource coupling,lengthy solution generation times,and poor inter-platform coll... In response to the challenges faced by unmanned swarms in mountain obstacle-breaching missions within complex terrains,such as poor task-resource coupling,lengthy solution generation times,and poor inter-platform collaboration,an unmanned swarm scheduling strategy tailored is proposed for mountain obstacle-breaching missions.Initially,by formalizing the descriptions of obstacle breaching operations,the swarm,and obstacle targets,an optimization model is constructed with the objectives of expected global benefit,timeliness,and task completion degree.A meta-task decomposition and reassembly strategy is then introduced to more precisely match the capabilities of unmanned platforms with task requirements.Additionally,a meta-task decomposition optimization model and a meta-task allocation operator are incorporated to achieve efficient allocation of swarm resources and collaborative scheduling.Simulation results demonstrate that the model can accurately generate reasonable and feasible obstacle breaching execution plans for unmanned swarms based on specific task requirements and environmental conditions.Moreover,compared to conventional strategies,the proposed strategy enhances task completion degree and expected returns while reducing the execution time of the plans. 展开更多
关键词 mountain obstacle breaching unmanned swarm task scheduling meta-task
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Microgrid Scheduling with the Participation of Electric Vehicles under Extreme Weather Conditions 认领 引用
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作者 Zujun Ding Zhi Liu +7 位作者 Peng Huang Yuhan Qian Chengyi Li Zizhuo Yu Hui Huang Baolian Liu Wan Chen Jie Ji 《Energy Engineering》 EI 2026年第6期363-392,共30页
Under extreme weather conditions(such as hurricanes and heatwaves causing sudden drops in renewable energy output and surges in load),microgrid operations face severe challenges due to the uncertainty of renewable ene... Under extreme weather conditions(such as hurricanes and heatwaves causing sudden drops in renewable energy output and surges in load),microgrid operations face severe challenges due to the uncertainty of renewable energy and load fluctuations.Although existing research has focused on microgrid optimal scheduling or electric vehicle integration,there has not yet been a systematic approach to multi-timescale scheduling that combines electric vehicle fleets under extreme weather scenarios,and particularly,explicit modeling of weather events and their impact on component failure rates and transmission lines is lacking.This paper proposes,for the first time,a multi-timescale optimal scheduling strategy integrated with an electric vehicle fleet,filling this gap.By constructing a microgrid model containing diesel generators,micro gas turbines,renewable energy sources,energy storage,and demand response loads,and defining four typical extreme weather scenarios(high solar&high wind,high solar&low wind,low solar&high wind,low solar&low wind)to simulate the impact of extreme events,a day-ahead and intraday coordinated framework aiming to minimize total operating costs is established.In this framework,the day-ahead stage formulates a preliminary plan based on wind and solar forecasts,while the intraday stage employs the mobile energy storage characteristics of the electric vehicle fleet for rolling adjustments to cope with renewable fluctuations and sudden load changes.Simulations based on actual data from Huai’an City in 2024 show that this strategy can significantly reduce microgrid operating costs(by 5.6%–7.2%),increase renewable energy utilization(94%–96%),reduce carbon emissions(17.8%–22.6%),and enhance the system’s economic performance and resilience under extreme weather conditions. 展开更多
关键词 Microgrid electric vehicle cluster multi-time scale scheduling extreme weather renewable energy absorption optimal operation demand response
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Aero-engine fleets scheduling optimization and task assignment under task-constraints 认领 引用
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作者 Xiangzhao XIA Xuyun FU Shisheng ZHONG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第6期355-388,共34页
The scheduling optimization and task assignment of aero-engine fleets are complex and dynamic,presenting a significant challenge in aviation engineering.This study proposes a Dual-layer Collaborative Optimization Fram... The scheduling optimization and task assignment of aero-engine fleets are complex and dynamic,presenting a significant challenge in aviation engineering.This study proposes a Dual-layer Collaborative Optimization Framework(DCOF)to address these challenges.The problem is decomposed into two interrelated sub-problems,scheduling optimization and task assignment,with distinct mathematical models formulated for each.For scheduling optimization,this study proposes an Improved Gravity Particle Swarm Optimization(IGPSO)algorithm.The algorithm enhances global search capability and convergence speed through dynamic weight adjustment and constraint processing repair strategies,effectively handling dynamic variations in engine health and remaining life.For task assignment,an Improved Branch-and-Price(IB&P)method is used.This method combines column generation with branch-and-bound strategies,while integrating heuristic rules and parallel computing techniques to efficiently find optimal solutions under multi-dimensional constraints.By clearly distinguishing between operational and maintenance tasks and considering their interdependencies,the proposed DCOF better captures real operational needs,improving fleet scheduling efficiency and reliability.Experimental validation and engineering simulations confirm the method’s effectiveness,showing advantages in repair balance,task assignment balance,and minimizing engine life waste.The approach enhances both usage efficiency and maintenance management of aero-engine fleets. 展开更多
关键词 Aero-engine fleets Improved branch-and-price Improved gravity particle swarm optimization Scheduling optimization Task assignment
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Evaluation of the impact of an interdisciplinary team scheduling model on psychological outcomes in patients with decompensated cirrhosis 认领 引用
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作者 Wei-Ying Xu Ye-Qin Li +2 位作者 Xiu-Ping Wei Hai-Ping Qin Li-Fan Feng 《World Journal of Hepatology》 2026年第1期141-151,共11页
BACKGROUND Patients with decompensated cirrhosis frequently experience severe psychological distress,anxiety,and depression,yet psychological support is often fragmented in conventional care.AIM To investigate the eff... BACKGROUND Patients with decompensated cirrhosis frequently experience severe psychological distress,anxiety,and depression,yet psychological support is often fragmented in conventional care.AIM To investigate the effect of interdisciplinary team scheduling on psychological outcomes in decompensated cirrhosis.METHODS A randomized,single-blind,single-center trial was conducted from January 2022 to December 2024 in Guangxi Zhuang Autonomous Region.A total of 110 patients with decompensated cirrhosis(Distress Thermometer≥4)were randomized to interdisciplinary team scheduling(n=55)or conventional scheduling(n=55).Psychological distress,anxiety,depression,and quality of life were assessed using the Distress Thermometer,Self-Rating Anxiety Scale,Self-Rating Depression Scale,and World Health Organization Quality of Life 100 questionnaire,respectively.RESULTS Following the intervention,the interdisciplinary group achieved significantly lower psychological distress[3(2-3)vs 3(3-4)],anxiety(41.65±4.29 vs 46.38±4.18),and depression scores(45.79±3.25 vs 50.14±3.69)compared with the control group(all P<0.05).Quality of life scores also improved significantly in the physical,psychological,and social domains(P<0.05).CONCLUSION The interdisciplinary team scheduling model effectively alleviates psychological symptoms and enhances quality of life among patients with decompensated cirrhosis.This model addresses unmet psychosocial needs through early,continuous,and collaborative care,providing a practical framework for integrating psychological support into chronic liver disease management. 展开更多
关键词 Decompensated cirrhosis Interdisciplinary collaborative team Nursing scheduling model Psychological outcome indicators Patient-centered care Chronic disease management
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Optimal scheduling of active distribution networks based on multi-scenario fuzzy set based charging station resource prediction 认领 引用
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作者 Zhang Maosong Zhang Chunyu +3 位作者 Hao Shi Yang Jie Yang Lingxiao Wang Xiuqin 《High Technology Letters》 EI CAS 2026年第1期97-108,共12页
With the large-scale integration of new energy sources,various resources such as energy storage,electric vehicles(EVs),and photovoltaics(PV) have participated in the scheduling of active distribution networks(ADNs),po... With the large-scale integration of new energy sources,various resources such as energy storage,electric vehicles(EVs),and photovoltaics(PV) have participated in the scheduling of active distribution networks(ADNs),posing new challenges to the operation and scheduling of distribution networks.Aiming at the uncertainty of PV and EV,an optimal scheduling model for ADNs based on multi-scenario fuzzy set based charging station resource forecasting is constructed.To address the scheduling uncertainties caused by PV and load forecasting errors,a day-ahead optimal scheduling model based on conditional value at risk(CVaR) for cost assessment is established,with the optimization objectives of minimizing the operation cost of distribution networks and the risk cost caused by forecasting errors.An improved subtractive optimizer algorithm is proposed to solve the model and formulate day-ahead optimization schemes.Secondly,a forecasting model for dispatchable resources in charging stations is constructed based on event-based fuzzy set theory.On this basis,an intraday scheduling model is built to comprehensively utilize the dispatchable resources of charging stations to coordinate with the output of distributed power sources,achieving optimal scheduling with the goal of minimizing operation costs.Finally,an experimental scenario based on the IEEE-33 node system is designed for simulation verification.The comparison of optimal scheduling results shows that the proposed method can fully exploit the potential scheduling resources of charging stations,improving the operation stability of ADNs and the accommodution capacity of new energy. 展开更多
关键词 charging station resource prediction subtractive optimizer algorithm multi-scenario fuzzy set two-stage optimal scheduling distribution network cost optimization
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An optimization scheduling strategy for electric-heat-hydrogen integrated energy systems based on memory-enhanced deep reinforcement learning 认领 引用
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作者 Zhongli BAI Qiang GAO +4 位作者 Hongzhi ZHANG Junjie LIU Yuehui JI Yu SONG Xu CHENG 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2026年第8期809-824,I0004-I0014,共16页
High wind-solar penetration drives integrated energy systems(IESs)to act as cross-vector buffers that absorb surplus electricity via hybrid storage,or convert it into heat and hydrogen for cross-medium energy peak sha... High wind-solar penetration drives integrated energy systems(IESs)to act as cross-vector buffers that absorb surplus electricity via hybrid storage,or convert it into heat and hydrogen for cross-medium energy peak shaving.However,conventional mixed-integer linear programming(MILP)solvers and static incentive schemes often struggle with the high-dimensional,strongly coupled,non-convex,and time-varying nature of electric-heat-hydrogen scheduling.Thus,we propose an end-to-end optimization framework for a renewable electric-heat-hydrogen IES,and evaluate it through offline dispatch computations and cost settlement on a 24-h simulated case study.The proposed framework incorporates a coupled power-state of charge(SOC)dual penalty mechanism to ensure consistent storage operation over time.Additionally,it includes a bidirectional incentive-based demand response(B-IDR)mapping that is differentiable and can capture asymmetric feedback in response to price fluctuations.Furthermore,a long short-term memory(LSTM)-augmented maximum-entropy soft actor-critic(SAC)scheduler is utilized for stable and efficient control in this continuous and high-dimensional setting.Comparisons with other methods under identical settings show that the proposed method achieves lower operating costs and carbon emissions,as well as improved training stability. 展开更多
关键词 Integrated energy system(IES) Electric-heat-hydrogen Long short-term memory(LSTM) Soft actor-critic(SAC) Reinforcement learning Scheduling strategy
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Increasing the Response Speed Without Redesigning the System:A Reference Input Scheduling Approach 认领 引用
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作者 Zongli Lin 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第1期1-2,共2页
WE observe that the response speed of a linear timeinvariant system to a step reference input depends not only on the system parameters but also on the magnitude of the step input.Based on this observation,we demonstr... WE observe that the response speed of a linear timeinvariant system to a step reference input depends not only on the system parameters but also on the magnitude of the step input.Based on this observation,we demonstrate a method to schedule the magnitude of the reference input to achieve a faster response. 展开更多
关键词 schedule magnitude reference input reference input scheduling linear timeinvariant system response speed linear time invariant system step input system parameters step reference input
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Review on Multi-objective Dynamic Scheduling Methods for Flexible Job Shops and Application in Aviation Manufacturing 认领 引用
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作者 MA Yajie JIANG Bin +3 位作者 GUAN Li CHEN Lijun HUANG Binda CHEN Zhi 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2025年第1期1-24,共24页
Intelligent production is an important development direction in intelligent manufacturing,with intelligent factories playing a crucial role in promoting intelligent production.Flexible job shops,as the main form of in... Intelligent production is an important development direction in intelligent manufacturing,with intelligent factories playing a crucial role in promoting intelligent production.Flexible job shops,as the main form of intelligent factories,constantly face dynamic disturbances during the production process,including machine failures and urgent orders.This paper discusses the basic models and research methods of job shop scheduling,emphasizing the important role of dynamic job shop scheduling and its response schemes in future research.A multi-objective flexible job shop dynamic scheduling mathematical model is established,highlighting its complex and multi-constraint characteristics under different interferences.A classification discussion is conducted on the dynamic response methods and optimization objectives under machine failures,emergency orders,fuzzy completion times,and mixed dynamic events.The development process of traditional scheduling rules and intelligent methods in dynamic scheduling are also analyzed.Finally,based on the current development status of job shop scheduling and the requirements of intelligent manufacturing,the future development trends of dynamic scheduling in flexible job shops are proposed. 展开更多
关键词 flexible job shop dynamic scheduling machine breakdown job insertion multi-objective optimization
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