Hybridization of Metaheuristics Based Energy Efficient Scheduling Algorithm for Multi-Core Systems

The developments of multi-core systems(MCS)have considerably improved the existing technologies in thefield of computer architecture.The MCS comprises several processors that are heterogeneous for resource capacities,working environments,topologies,and so on.The existing multi-core technology unlocks additional research opportunities for energy minimization by the use of effective task scheduling.At the same time,the task scheduling process is yet to be explored in the multi-core systems.This paper presents a new hybrid genetic algorithm(GA)with a krill herd(KH)based energy-efficient scheduling techni-que for multi-core systems(GAKH-SMCS).The goal of the GAKH-SMCS tech-nique is to derive scheduling tasks in such a way to achieve faster completion time and minimum energy dissipation.The GAKH-SMCS model involves a multi-objectivefitness function using four parameters such as makespan,processor utilization,speedup,and energy consumption to schedule tasks proficiently.The performance of the GAKH-SMCS model has been validated against two datasets namely random dataset and benchmark dataset.The experimental outcome ensured the effectiveness of the GAKH-SMCS model interms of makespan,pro-cessor utilization,speedup,and energy consumption.The overall simulation results depicted that the presented GAKH-SMCS model achieves energy effi-ciency by optimal task scheduling process in MCS.

A QoS-Aware Packet Scheduling Mechanism in Cognitive Radio Networks for Smart Grid Applications

Cognitive radio sensor network is applied to facilitate network monitoring and management, and achieves high spectrum efficiencies in smart grid. However, the conventional traffic scheduling mechanisms are hard to provide guaranteed quality of service for the secondary users. It is because that they ignore the influence of diverse transition requirements in heterogeneous traffi c. Therefore, a novel Qo S-aware packet scheduling mechanism is proposed to improve transmission quality for secondary users. In this mechanism, a Qo S-based prioritization model is established to address data classification firstly. And then, channel quality and the effect of channel switch are integrated into priority-based packet scheduling mechanism. At last, the simulation is implemented with MATLAB and OPNET. The results show that the proposed scheduling mechanism improves the transmission quality of high-priority secondary users and increase the whole system utilization by 10%.

Parallel scheduling strategy of web-based spatial computing tasks in multi-core environment

In order to improve the concurrent access performance of the web-based spatial computing system in cluster,a parallel scheduling strategy based on the multi-core environment is proposed,which includes two levels of parallel processing mechanisms.One is that it can evenly allocate tasks to each server node in the cluster and the other is that it can implement the load balancing inside a server node.Based on the strategy,a new web-based spatial computing model is designed in this paper,in which,a task response ratio calculation method,a request queue buffer mechanism and a thread scheduling strategy are focused on.Experimental results show that the new model can fully use the multi-core computing advantage of each server node in the concurrent access environment and improve the average hits per second,average I/O Hits,CPU utilization and throughput.Using speed-up ratio to analyze the traditional model and the new one,the result shows that the new model has the best performance.The performance of the multi-core server nodes in the cluster is optimized;the resource utilization and the parallel processing capabilities are enhanced.The more CPU cores you have,the higher parallel processing capabilities will be obtained.

Event-driven Dynamic and Intelligent Scheduling for Agile Manufacturing Based on Immune Mechanism and Expert System

Based on the biological immune concept, immune response mechanism and expert system, a dynamic and intelligent scheduling model toward the disturbance of the production such as machine fault,task insert and cancel etc. Is proposed. The antibody generation method based on the sequence constraints and the coding rule of antibody for the machining procedure is also presented. Using the heuristic antibody generation method based on the physiology immune mechanism, the validity of the scheduling optimization is improved, and based on the immune and expert system under the event-driven constraints, not only Job-shop scheduling problem with multi-objective can be solved, but also the disturbance of the production be handled rapidly. A case of the job-shop scheduling is studied and dynamic optimal solutions with multi-objective function for agile manufacturing are obtained in this paper. And the event-driven dynamic rescheduling result is compared with right-shift rescheduling and total rescheduling.

Design of Scheduling Decision Mechanism for Agile Manufacturing System

Design of scheduling decision mechanism is a key issue of scheduling decision method and strategy for agile manufacturing system. Effective scheduling decision mechanism helps to improve the operational agility of manufacturing system. Several scheduling decision mechanisms are discussed, including scheduling forecasting mechanism, cooperation mechanism and cell scheduling mechanism. Also soft decision mechanism is put forward as a promising prospect for agile manufacturing system, and some key techniques in soft decision mechanism are introduced.

Multi-objective optimization in highway pavement maintenance and rehabilitation project selection and scheduling:A state-of-the-art review

The motivation for cost-effective management of highway pavements is evidenced not only by the massive expenditures associated with these activities at a national level but also by the consequences of poor pavement condition on road users.This paper presents a state-of-the-art review of multi-objective optimization(MOO)problems that have been formulated and solution techniques that have been used in selecting and scheduling highway pavement rehabilitation and maintenance activities.First,the paper presents a taxonomy and hierarchy for these activities,the role of funding sources,and levels of jurisdiction.The paper then describes how three different decision mechanisms have been used in past research and practice for project selection and scheduling(historical practices,expert opinion,and explicit mathematical optimization)and identifies the pros and cons of each mechanism.The paper then focuses on the optimization mechanism and presents the types of optimization problems,formulations,and objectives that have been used in the literature.Next,the paper examines various solution algorithms and discusses issues related to their implementation.Finally,the paper identifies some barriers to implementing multi-objective optimization in selecting and scheduling highway pavement rehabilitation and maintenance activities,and makes recommendations to overcome some of these barriers.

Hierarchical Disturbance Propagation Mechanism and Improved Contract Net Protocol for Satellite TT&C Resource Dynamic Scheduling

The practical engineering of satellite tracking telemetry and command(TT&C)is often disturbed by unpredictable external factors,including the temporary rise in a significant quantity of satellite TT&C tasks,temporary failures and failures of some TT&C resources,and so on.To improve the adaptability and robustness of satellite TT&C systems when faced with uncertain dynamic disturbances,a hierarchical disturbance propagation mechanism and an improved contract network dynamic scheduling method for satellite TT&C resources were designed to address the dynamic scheduling problem of satellite TT&C resources.Firstly,the characteristics of the dynamic scheduling problem of satellite TT&C resources are analyzed,and a mathematical model is established with the weighted optimization objectives of maximizing the revenue from task completion and minimizing the degree of plan disturbance.Then,a bottom-up distributed dynamic collaborative scheduling framework for satellite TT&C resources is proposed,which includes a task layer,a resource layer,a central internal collaboration layer,and a central external collaboration layer.Dynamic disturbances are propagated layer by layer from the task layer to the central external collaboration layer in a bottom-up manner,using efficient heuristic strategies in the task layer and the resource layer,respectively.We use improved contract network algorithms in the center internal collaboration layer and the center external collaboration layer,the original scheduling plan is quickly adjusted to minimize the impact of disturbances while effectively completing dynamic task requirements.Finally,a large number of simulation experiments were carried out and compared with various comparative algorithms.The results show that the proposed algorithm can effectively improve the solution effect of satellite TT&C resource dynamic scheduling problems,and has good application prospects.

Solving Job-Shop Scheduling Problem Based on Improved Adaptive Particle Swarm Optimization Algorithm

An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal factor(HF),composed of an adaptive local hormonal factor(H l)and an adaptive global hormonal factor(H g),is devised to strengthen the information connection between particles.Using HF,each particle of the swarm can adjust its position self-adaptively to avoid premature phenomena and reach better solution.The computational results validate the effectiveness and stability of the proposed IAPSO,which can not only find optimal or close-to-optimal solutions but also obtain both better and more stability results than the existing particle swarm optimization(PSO)algorithms.

Optimization Design of Double-parameter Shift Schedule of Tracked Vehicle with Hydrodynamic-mechanical Transmission

A kind of automatic shift schedule optimization method is provided for a tracked vehicle with hydrodynamic-mechanical transmission in order to improve its dynamic performance. A dynamic model of integrated hydrodynamic-mechanical transmission is built in MATLAB/Simdriveline environment, and an optimum shift schedule is derived by using iSight software to call the dynamic model above, then the shift schedule is achieved after optimization. The simulation results show that the method is significant to improve the dynamic performance and gear-shifting smoothness theoretically and practically.

Dual-resource integrated scheduling method of AGV and machine in intelligent manufacturing job shop

In view of the fact that traditional job shop scheduling only considers a single factor, which affects the effect of resource allocation, the dual-resource integrated scheduling problem between AGV and machine in intelligent manufacturing job shop environment was studied. The dual-resource integrated scheduling model of AGV and machine was established by comprehensively considering constraints of machines, workpieces and AGVs. The bidirectional single path fixed guidance system based on topological map was determined, and the AGV transportation task model was defined. The improved A* path optimization algorithm was used to determine the optimal path, and the path conflict elimination mechanism was described. The improved NSGA-Ⅱ algorithm was used to determine the machining workpiece sequence, and the competition mechanism was introduced to allocate AGV transportation tasks. The proposed model and method were verified by a workshop production example, the results showed that the dual resource integrated scheduling strategy of AGV and machine is effective.

A Dynamic Job Shop Scheduling Method Based on Ant Colony Coordination System

Due to the stubborn nature of dynamic job shop scheduling problem,a novel ant colony coordination mechanism is proposed in this paper to search for an optimal schedule in dynamic environment.In ant colony coordination mechanism,the dynamic job shop is composed of several autonomous ants.These ants coordinate with each other by simulating the ant foraging behavior of spreading pheromone on the trails,by which they can make information available globally,and further more guide ants make optimal decisions.The proposed mechanism is tested by several instances and the results confirm the validity of it.

基于主动尾流控制的风电机群协同优化调度

针对机组间尾流效应严重影响风电机组发电效率的问题,提出了风电机组安全偏航约束计算方法、尾流特性混合半机理建模方法以及风电机群多目标协同优化调度方法。基于FAST.FARM平台完善了多自由度可控机组与尾流的动态交互集成仿真环境,对比分析了2台机组串列式排布以及华东地区某海上风电场7台机组实际排布下的协同运行优化性能。结果表明:所建立的集成仿真模型能够合理表征风电机群与空气流场的多领域动态交互特性,所提方法能够有效提升风电机群发电效能,促进经济效益、资源利用和成本控制的均衡优化。

多源遥感卫星地面调度系统设计与实现

针对卫星地面调度处理系统进行研究与设计,着重关注工作流、线程池和Redis缓存等关键技术。首先,对工作流的分析和建模以提高系统任务的执行效率和资源利用率。其次,引入线程池技术来管理和调度系统中的线程资源,实现任务的并发执行和负载均衡,提高系统的吞吐量和响应速度。最后,利用Redis缓存作为系统的缓存和Kafka消息队列,优化数据存取和通信效率,提供更高的性能和可靠性。整体通过SpringBoot框架进行实验和性能评估,验证了该系统在任务处理能力、资源利用率和数据访问效率等方面的优势。本研究对于提高卫星地面调度处理系统的效率和性能具有一定的实践意义和推广价值。

考虑氢能利用与需求响应的综合能源系统低碳优化调度

综合能源系统(IES)对提高能源利用效率、减少碳排放具有重要意义.为了更好地实现这一目标,提出一种考虑氢能利用与需求响应的IES低碳优化调度方法.在源侧,构建以氢能利用为核心的IES模型来优化设备运行灵活性;在负荷侧,考虑到用户用能特征,建立基于Logistic函数的需求响应模型来优化负荷曲线、协助降碳;同时,为进一步挖掘系统的碳减排潜力,在优化模型中引入阶梯式碳交易机制;最后,综合考虑系统购能成本、运维成本、碳交易成本以及弃风成本,以IES日运行总成本最小为目标函数进行优化调度.算例验证结果表明,所构建模型不仅达到负荷削峰填谷的效果,同时可以有效地降低IES的运行总成本和碳排放,具有较好的低碳经济性.

基于5G技术的舰船电力调度自动化系统研究

考虑舰船电力系统线路复杂、设备多样,调度数据冗杂且规模巨大,在此情况下为实现舰船电力系统快速、有效自动化调度,设计基于5G技术的舰船电力调度自动化系统。系统使用由RTU远动终端设备采集电力系统的线路、设备运行信息后,由5G通信网关发送至调度器;调度器结合目前电力系统运行信息,使用基于线性求解器的电力系统自动化调度模型,构建以快速实现断电负载故障合理恢复为调度目标的目标函数,由线性求解器自动化求解满足目标函数条件的电力系统开关动作方案,以故障重构的方式实现舰船电力调度自动化管理。实验中该系统调度下,舰船电力系统失电负荷为0 kW,5G通信网络的整体效用值不受调度数据业务数据包数量的约束,高达0.9,能够稳定、高效地支持大规模调度数据的传输。

计及碳配额的电动公交车⁃配电网协同优化调度策略

考虑个人碳交易机制在电动公交车优化调度中的应用,提出一种基于动态碳价格的碳配额激励机制,电动公交车用户可以通过出售碳配额获取一定的收益。首先,计及路况对电动公交车运行时间产生的影响,根据工作日与休息日交通指数对电动公交车进站时间进行修正,构建电动公交车工作日与休息日充电负荷模型;然后,将基于动态碳价格的碳配额激励机制运用于电动公交车与新能源协同调度策略,构建综合考虑用户与电网利益的多目标函数,并运用鲸鱼算法对目标函数进行求解;最后,基于经济指标和性能指标,对固定碳价格、动态碳价格两种激励机制进行对比分析,通过算例验证了基于动态碳价格的碳配额激励机制的电动公交车与新能源协同调度策略的优越性。

融合蚁群算法框架下区域资源的动态调度模型

针对目前电力作业人力资源调度主要依赖于人工经验的现状,文中提出了基于改进蚁群算法的区域资源动态调度模型。该模型以花费总时间与总成本最小为优化目标,考虑了任务的资源需求、先后顺序等方面的约束条件。同时,结合区域资源动态调度模型的特性,对传统蚁群算法进行了改进设计,提出了优化的编码解码方法、蚁群动态更新机制及2-opt局部搜索方法等策略。算例分析结果表明,所提改进蚁群算法相比于传统蚁群算法具有更快的收敛速度和更高的计算准确度。在实际电力作业调度中,对比传统蚁群算法,所设计模型能够减少总时间约12%,降低总成本约10%。

考虑柔性负荷和阶梯式碳交易的微电网鲁棒优化调度

针对低碳背景下微电网中可再生能源出力的不确定性以及碳排放问题,提出了一种考虑阶梯式碳交易机制同时柔性负荷参与调控的微电网两阶段鲁棒优化方法。在调度模型中引入阶梯式碳交易机制,限制系统的碳排放量;通过柔性负荷的调控作用弥补由于可再生能源出力波动造成的功率缺额,进一步减小了微电网的碳排放量和调度成本;模型中引入不确定参数调节系统的保守性,日前阶段基于预测数据并考虑系统日内可能遭受的最恶劣场景,制定预测数据下的日前调度方案,日内调控阶段则在日前调度方案的基础上进行二次优化,在最恶劣场景下给出系统的日内调控方案;模型利用嵌套C&CG算法对调度模型进行求解。算例仿真结果证明,鲁棒优化方法相较于确定性优化方法其总调度成本平均减少了3.1%;考虑阶梯式碳交易机制后,系统单日运行的碳排放量和总调度成本相较于未考虑碳交易机制时分别下降了17.7%和3.7%;考虑柔性负荷参与调度后,系统单日运行的碳排放量和总调度成本相较于未考虑柔性负荷时分别下降了26%和24.3%,验证了所提优化模型的有效性。

园区综合能源系统低碳经济优化调度模型研究

现有的园区综合能源系统调度方案,存在成本高、污染环境且能源利用率低等问题。因此,提出一种计及电转气和碳交易机制的园区综合能源系统低碳经济优化调度模型。首先,重点分析电转气过程甲烷化环节强放热特性,研究其对系统优化调度的影响;其次,构建含补偿系数的综合园区能源系统阶梯式碳交易机制,以约束园区碳排放;最后,建立以运行成本、弃能功率、碳排放量最小为目标的园区综合能源系统优化调度模型,并进行多目标优化计算。结果表明:所提优化模型的总成本、弃能功率和碳排放量分别下降了15.4%、98.3%和19.7%。因此,该文所提园区综合能源系统优化调度模型提高了对能源的利用效率和经济性,并有效降低了园区的碳排放。