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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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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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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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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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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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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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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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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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Information Diffusion Models and Fuzzing Algorithms for a Privacy-Aware Data Transmission Scheduling in 6G Heterogeneous ad hoc Networks 认领 引用 被引量：1
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作者 Borja Bordel Sánchez Ramón Alcarria Tomás Robles 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第2期1214-1234,共21页
In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic h... In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic heterogeneous infrastructures,unstable links and non-uniform hardware capabilities create critical issues regarding security and privacy.Traditional protocols are often too computationally heavy to allow 6G services to achieve their expected Quality-of-Service(QoS).As the transport network is built of ad hoc nodes,there is no guarantee about their trustworthiness or behavior,and transversal functionalities are delegated to the extreme nodes.However,while security can be guaranteed in extreme-to-extreme solutions,privacy cannot,as all intermediate nodes still have to handle the data packets they are transporting.Besides,traditional schemes for private anonymous ad hoc communications are vulnerable against modern intelligent attacks based on learning models.The proposed scheme fulfills this gap.Findings show the probability of a successful intelligent attack reduces by up to 65%compared to ad hoc networks with no privacy protection strategy when used the proposed technology.While congestion probability can remain below 0.001%,as required in 6G services. 展开更多
关键词 6G networks ad hoc networks privacy scheduling algorithms diffusion models fuzzing algorithms
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Adaptive Net-Profit-Based Scheduling with Minimizing Mutual Exclusion vRB Allocation in 5G-A NR Networking 认领 引用 被引量：1
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作者 Wei-Teng Chang Ben-Jye Chang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期1290-1318,共29页
Some critical applications of emergency,Active Safe Driving(ASD),eV2X,and LEO communications require ultra-low delay and highly reliable transmission according to beyond 5G-Advanced(5G-A),6G,and LEO specifications.Rel... Some critical applications of emergency,Active Safe Driving(ASD),eV2X,and LEO communications require ultra-low delay and highly reliable transmission according to beyond 5G-Advanced(5G-A),6G,and LEO specifications.Related studies proposed various scheduling algorithms in terms of single and multiple QoS requirements.However,these approaches tend to prioritize traditional QoS requirements while neglecting crucial considerations such as bearer costs and associated benefits.Moreover,most scheduling neglects the carrying cost according to the radio resource state and the bringing reward from different types of flows.Thus,this paper proposes a novel cost-based flow scheduling(eSCFS)framework that utilizes an extended sigmoid function to dynamically prioritize flows,taking into account all relevant key factors.The principal objective is to reduce latency while optimizing the utilization of radio RB and maximizing the net benefits of 5G-A NR networks.The eSCFS method has been validated through numerical simulations,which demonstrated superior key performance metrics,including network latency,resource utilization,and overall profitability.Consequently,several objectives are thus achieved:1)analyzing the QoS requirements of various services within limited radio resources,2)proposing a novel vRB state-dependent dynamic flow scheduling and adaptive virtual radio RB management to maximize network performance. 展开更多
关键词 5G-advanced 6G frequency numerology flow scheduling radio RB
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A Workflow Scheduling Method Based on the Combination of Tunicate Swarm Algorithm and Highest Response Ratio Next Scheduling 认领 引用
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作者 Yujie Tian Ming Zhu +2 位作者 Jing Li Cong Liu Ziyang Zhang 《Computers, Materials & Continua》 SCIE EI 2026年第5期1950-1963,共14页
Workflow scheduling is critical for efficient cloud resource management.This paper proposes Tunicate Swarm-Highest Response Ratio Next,a novel scheduler that synergistically combines the Tunicate Swarm Algorithm with ... Workflow scheduling is critical for efficient cloud resource management.This paper proposes Tunicate Swarm-Highest Response Ratio Next,a novel scheduler that synergistically combines the Tunicate Swarm Algorithm with the Highest Response Ratio Next policy.The Tunicate Swarm Algorithm generates a cost-minimizing task-to-VM mapping scheme,while the Highest Response Ratio Next dynamically dispatches tasks in the ready queue with the highest-priority.Experimental results demonstrate that the Tunicate Swarm-Highest Response RatioNext reduces costs by up to 94.8%compared to meta-heuristic baselines.It also achieves competitive cost efficiency vs.a learning-based method while offering superior operational simplicity and efficiency,establishing it as a highly practical solution for dynamic cloud environments. 展开更多
关键词 Workflow scheduling cloud computing tunicate swarm algorithm highest response ratio next scheduling
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A Two-Step Iterative Local Search for the Harvester Scheduling Problem With Splittable Workloads 认领 引用
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作者 Hongyu Lin Jing Liang +2 位作者 Bo Jin Caitong Yue Yaonan Wang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第7期1584-1599,共16页
With the growing emphasis on intelligent agriculture,scheduling problems involving multiple harvesters in largescale croplands have attracted increasing attention.In these scenarios,harvesters collaboratively perform ... With the growing emphasis on intelligent agriculture,scheduling problems involving multiple harvesters in largescale croplands have attracted increasing attention.In these scenarios,harvesters collaboratively perform tasks in expansive croplands,with each harvester taking on a portion of the workload.This study defines such problems as the harvester scheduling problem with splittable workloads(HSPSW).Unlike most multi-robot task allocation problems,HSPSW requires collaboration among several harvesters and involves more harvesters than croplands.These features increase the complexity of harvester-cropland interactions.To address these challenges,this paper proposes a novel two-step iterative local search(TSILS)algorithm.A greedy strategy that prioritizes croplands with higher workloads is proposed to generate initial solutions.Subsequently,a two-step iterative search strategy is employed to obtain high-quality solutions:the first step uses a predefined objective to facilitate rapid workload allocation for feasible solutions,and the second step integrates specific neighborhood operators and finegrained local search to improve its search capability.Experimental results demonstrate that the proposed method TSILS significantly outperforms existing approaches in both algorithmic performance and computational efficiency. 展开更多
关键词 Cooperative operations heuristic local search multi-harvester scheduling
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An Intelligent Algorithm for Dynamic Scheduling of Parallel Machines Considering Multi-Task Collaboration in Order Processing 认领 引用
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作者 Pei Xie Xiaoying Yang +2 位作者 Bo Li Zhijie Pei Fenghai Yang 《Computers, Materials & Continua》 SCIE EI 2026年第9期813-836,共24页
To address the critical requirements for collaborative delivery of multiple tasks within each order in personalized mass customization,this paper develops a dynamic parallel machine scheduling model that accounts for ... To address the critical requirements for collaborative delivery of multiple tasks within each order in personalized mass customization,this paper develops a dynamic parallel machine scheduling model that accounts for stochastic machine failures and order priorities,thereby more accurately reflecting the uncertainties and complexities of real-world production environments.A dual-objective optimization framework is adopted to minimize both the makespan(maximum task completion time)and the variance of task completion times,aiming to improve the coordination and reliability of intra-order task delivery.An adaptive weighted reward function is designed to balance overall scheduling efficiency with consistency among tasks during reinforcement learning training.To tackle the challenges posed by partially observable Markov decision processes(POMDP)induced by unexpected machine breakdowns,a Gated Recurrent Unit(GRU)-embedded Proximal Policy Optimization(PPO)intelligent scheduling algorithm is proposed.The algorithm incorporates an Action Masking mechanism to prevent invalid scheduling actions,while the GRU module captures historical state sequences to enhance perception of dynamic production environments.Extensive validation on benchmark datasets,along with comparisons against traditional heuristic algorithms,metaheuristic algorithms,and other deep reinforcement learning methods,demonstrates that the proposed approach achieves robust convergence,high resilience,and strong generalization across both static and dynamic scenarios,significantly improving coordinated delivery performance of order tasks.Overall,the proposed method not only provides an efficient and scalable real-time decision-making solution for Parallel Machine Scheduling Problems(PMSP)but also offers new theoretical and practical insights for optimizing complex production scheduling in intelligent manufacturing systems. 展开更多
关键词 Parallel machine scheduling problems dynamic scheduling gated recurrent unit proximal policy optimization coordinated delivery
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Modeling and Optimization of Diffusion Process Scheduling under Strict Queue Time Constraints in Semiconductor Manufacturing 认领 引用
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作者 Liangchao Chen Yan Qiao +3 位作者 Siwei Zhang Bin Liu Yonghua Shao Sijun Zhan 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期488-519,共32页
This article examines wafer lots scheduling in the diffusion area in semiconductor manufacturing.The diffusion area comprises multiple tool groups.Each of them contains non-identical semiconductor tools.All tools can ... This article examines wafer lots scheduling in the diffusion area in semiconductor manufacturing.The diffusion area comprises multiple tool groups.Each of them contains non-identical semiconductor tools.All tools can process multiple wafer lots simultaneously,and wafer lots processed together in a tool are called a wafer batch.Besides,each wafer lot has specific queue time limits(QTLs)between consecutive processing operations,making the scheduling problem more complicated.To solve it,a discrete backtracking search optimization algorithm(DBSA)is designed for optimizing both wafer lot assignments and wafer batch processing sequences.Once the processing sequence of wafer batches at each tool is determined,a linear program(LP)is built to obtain optimal starting and completion time points of wafer batches while satisfying QTLs.If a schedule is examined to have no feasible solution by the LP,a proposed approach is used to regroup wafer lots to form wafer batches and adjust their processing sequences to potentially make it feasible.Extensive experiments show that DBSA reliably produces feasible schedules and outperforms GA,MixPSO,and GWO,with up to 17.75%,19.19%,and 9.21%reductions in average cycle time,respectively,demonstrating its superiority in both solution quality and practical applicability. 展开更多
关键词 Metaheuristic algorithms queue time limits scheduling semiconductor manufacturing
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Triage:optimizing LLM serving goodput in unified scheduling via feedback-driven batch shaping 认领 引用
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作者 Lyu Cunchi Shi Xiao +2 位作者 Lei Zhengyu Li Qingwen Zhao Xiaofang 《High Technology Letters》 EI CAS 2026年第2期121-132,共12页
Existing iteration-level schedulers for large language model(LLM)serving rely on static,single-mode policies(e.g.,prefill-first or chunked-prefill)and fixed batching hyperparameters,which fail to adapt to the dynamic ... Existing iteration-level schedulers for large language model(LLM)serving rely on static,single-mode policies(e.g.,prefill-first or chunked-prefill)and fixed batching hyperparameters,which fail to adapt to the dynamic resource demands of production workloads.Consequently,these systems often struggle to balance the competing pressures of prefill and decode phases,leading to significant degradation in goodput,the rate of requests satisfied within strict service level objectives(SLOs).To address these limitations,we propose Triage,a unified scheduling framework that optimizes goodput via feedback-driven batch shaping.Unlike prior state-aware schedulers that operate in abstract design spaces,Triage leverages a hierarchical feedback control loop to directly manipulate concrete engine-level knobs,enabling dynamic switching between decode-only,chunked-prefill,and prefillonly regimes.By continuously monitoring workload composition and key value(KV)cache pressure,Triage dynamically steers batch composition and execution regimes without requiring manual hyperparameter tuning.We implement Triage on top of the vLLM,open-source LLM serving engine,and experimental results demonstrate that it achieves up to 10.50×higher goodput compared to state-of-the-art baselines while maintaining robust performance across diverse workload distributions and distributed serving configurations. 展开更多
关键词 large language model serving iteration-level scheduling goodput-driven scheduling
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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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