Cloud download system optimizing by job and notification scheduling

With expansion of cloud-based applications, it is necessarily for business service provider to deal with how to provision resources to server customers. In cloud download, the service provider faces great challenge on determining when to process job and when to notify the user to meet the conflicting objectives, i.e., the service profit maximization and user satisfaction maximization. An economic model was proposed to find job and notification scheduling strategies for making tradeoffs between user satisfaction and profit of the system, in this model, optimization problems are formulated as follows, profit maximization under a satisfaction target and then satisfaction optimization with a profit bound. By solving these optimization problems, the optimal scheduling strategies are obtained under various quality requirements of service. It is demonstrated that the proposed strategy can obtain better performance than the system without the intelligent scheduling strategy.     

信息系统在企业物流中的应用研究

随着经济的发展,物流在企业管理中的作用和影响越来越大,有的甚至决定了企业的盈亏。那么信息在企业物流中又起到什么作用呢?在企业的物流中工作流程又是怎样的?针对企业物流信息系统的应用现状,对企业物流业务流程和信息流程及结构进行了分析和研究,对未来现代企业如何进行科学化的物流管理提供一些参考意见。     

基于RFID的猪肉供应链可追溯系统研究

将RFID用于猪肉供应链中可以利用其存贮空间记录生猪饲养、屠宰、运输、销售等过程的数据,实现猪肉供应链的可追溯性.但在使用RFID时要注意确保其数据输入的真实性.     

物联网技术在B2C电子商务配送运输中的应用

随着电子商务的发展,传统的物流模式已无法满足业务需求,建立完善的运输车辆实时动态监控系统,实现对运输全过程的车辆以及货物状态的监测,是当前亟需解决的问题。采用物联网技术能够解决这一问题,可大幅提高车辆货物在运输过程中的安全性以及可靠性。文中对物联网相关技术进行阐述,重点探讨物联网技术在B2C电子商务配送运输中的应用设计以及实现的功能,分析了物联网技术对电子商务模式的影响。     

云边协同中的资源调度优化

随着业务类型的丰富和多样化，低时延、高带宽、数据私密性、高可靠性等成为业务普遍的要求。边缘计算、雾计算、分布式云、算力网络等方案相继被提出，并在产学研各界引发了深度的研究和探索。针对“多级的算力分布以及算力的协同将是未来算力结构的主流”这一观点，产业内外达成了共识，算力管理、分配、调度等与资源优化相关的问题也成为当下的研究热点和重点攻关方向。为此，面向未来的算力供给结构，首先描述了学术界、产业界资源调度优化问题的最新进展，总结了当前的主要方法论和工程实施架构；然后，针对两种典型的云边协同场景，从场景拆分、调度目标、求解方案依次进行分析，给出了适应场景特性的资源调度优化参考方案。     

基于物联网的物流业务流程再造设计

分析了物联网发展使物流服务功能发生结构上的变化，纵向流程压缩，横向流程拓展；阐述了物联网技术应用是物流业务体系再造的动力，也是业务层再造设计的条件；针对传统物流业务流程，提出了基于物联网技术体系的仓储、配送、运输业务流程优化和再造设计。     

道路交通网络节点分配优化策略研究进展

为了进一步提高城市道路交通网络的通行效率,粒子群优化和神经网络等多种智能优化算法受到越来越多的关注。近年来,深度学习技术的普及与应用大幅提升了城市交通网络的节点识别效率,而交通网络的节点调度又扩展了深度学习技术的应用。文中详细分析了交通节点调度所面临的关键问题,归纳并总结了相关网络节点分配的研究现状。在此基础上,深入研讨了城市交通网络节点调度与深度学习的应用前景,并对交通网络节点分配优化策略的未来研究方向进行了展望。     

NoC‐Based SoC Test Scheduling Using Ant Colony Optimization

In this paper, we propose a novel ant colony optimization (ACO)‐based test scheduling method for testing network‐on‐chip (NoC)‐based systems‐on‐chip (SoCs), on the assumption that the test platform, including specific methods and configurations such as test packet routing, generation, and absorption, is installed. The ACO metaheuristic model, inspired by the ant's foraging behavior, can autonomously find better results by exploring more solution space. The proposed method efficiently combines the rectangle packing method with ACO and improves the scheduling results by dynamically choosing the test‐access‐mechanism widths for cores and changing the testing orders. The power dissipation and variable test clock mode are also considered. Experimental results using ITC’02 benchmark circuits show that the proposed algorithm can efficiently reduce overall test time. Moreover, the computation time of the algorithm is less than a few seconds in most cases.     

Dynamic scheduling and analysis of real time systems with multiprocessors

This research work considers a scenario of cloud computing job-shop scheduling problems. We consider m realtime jobs with various lengths and n machines with different computational speeds and costs. Each job has a deadline to be met, and the profit of processing a packet of a job differs from other jobs. Moreover, considered deadlines are either hard or soft and a penalty is applied if a deadline is missed where the penalty is considered as an exponential function of time. The scheduling problem has been formulated as a mixed integer non-linear programming problem whose objective is to maximize net-profit. The formulated problem is computationally hard and not solvable in deterministic polynomial time. This research work proposes an algorithm named the Tube-tap algorithm as a solution to this scheduling optimization problem. Extensive simulation shows that the proposed algorithm outperforms existing solutions in terms of maximizing net-profit and preserving deadlines.     

基于遗传算法的内容编排模型设计

在支撑内容型业务及移动广告业务的内容管理系统中,内容编排是基于订单的业务媒介资源规划利益最大化的热点问题。将媒介资源构建成以空间模板分屏、时序资源、受众分组3个维度的载体业务模型,并且提出基于订单需求约束,以实现订单高效资源利用率、精确投放时间为目标的多目标资源优化(MOP)模型——内容编排模型,设计了并行遗传算法的模型求解算法并进行仿真,验证了模型的有效性。     

调度问题及其解空间的特征分析

目前,在组合优化领域中,评判近优算法的性能尚缺乏统一的标准和有效的依据,而算法的效率与所要解决问题之间的关系密不可分.本文以JSP问题为例,研究了调度问题本身的结构特征,分析了调度问题可行解空间的属性,提出了分割因子的概念.研究表明,分割因子影响调度问题可行解空间的规模,而各工序加工时间的分布则影响解空间的"崎岖"状况;分割因子和工件加工时间的分布在一定程度上可以反映调度问题的复杂程度.这对近优算法的设计具有一定的指导意义,并为建立统一的近优算法效率衡量标准迈出了探索性的一步.     

基于RFID的物流仓储管理系统分析与设计

针对传统仓储管理模式中普遍存在的业务流程多,跟踪困难,周转效率较低,物流管理信息处理不及时和手段落后,从而影响企业竞争力的问题,在分析传统物流仓储管理方式不足的基础上,提出了在物流仓储领域应用RFID技术的巨大优势来解决上述问题。对基于RFID的物流仓储管理信息系统进行了需求分析,通过对传统物流仓储业务流程进行分析研究,给出了物流仓储业务流程的信息和过程结构,并建立了基于RFID技术的仓储业务系统实施架构方法。     

基于供应链管理的物流信息系统架构

以中国石油炼化板块业务为背景,使用业务组件模型方法对公司的业务进行分析,并重点对物流业务进行分析,明确物流业务的信息化需求,采用国际先进石油化工物流企业三层信息系统架构,参照国内外石油化工物流行业信息技术应用发展趋势及最佳实践,形成基于供应链管理的物流信息系统建设方案,用于指导物流信息系统建设。     

云模型优化的QPSO算法在大数据云存储平台业务调度中的应用

QPSO算法作为最优秀的群体智能算法之一,有着较强的寻优能力,将QPSO算法用于云计算平台资源调度策略的寻优,有着明显的优势。通过对QPSO算法在云计算中的应用研究,为云计算平台效率的提升提供有效方法。文章对云模型优化的QPSO算法在大数据云存储平台业务调度中的应用进行分析与研究。     

网络融合环境下OTN的组网方式

在三网融合形势下,多媒体业务与数据传输需求不断增加,对传统骨干传输网络提出更高的要求。传统的基于WDM、SHD技术的光纤通信网络虽可满足传输容量需求,但存在成本较高、扩展和调度能力差、业务多样化能力弱等问题,不能进行有效的组网,难以满足未来发展的需要,因此选用新一代的OTN技术进行组网成为发展的必然趋势。通过相关资料研究,结合工作实践经验,对OTN技术的特点、OTN在光通信网络中的组网方式进行总结分析,以期促进OTN技术的应用与发展。     

甲板舰载机动态调度研究方法综述

舰载机甲板调度是受时间、空间、资源约束的多任务复杂问题,是航母作战效能的重要保证。文中主要剖析了舰载机调度作业的应用及特点,对比分析了最优化方法、仿真方法、Multi-agent方法以及智能搜索与规划方法等,舰载机动态调度研究方法的适用性,提出了采用智能规划研究方法是解决舰载机动态调度问题的主要途径之一,但在方案制定和舰载机调度过程中需加入指挥人员的经验。     

Agent-based distributed manufacturing process planning and scheduling: a state-of-the-art survey

Manufacturing process planning is the process of selecting and sequencing manufacturing processes such that they achieve one or more goals and satisfy a set of domain constraints. Manufacturing scheduling is the process of selecting a process plan and assigning manufacturing resources for specific time periods to the set of manufacturing processes in the plan. It is, in fact, an optimization process by which limited manufacturing resources are allocated over time among parallel and sequential activities. Manufacturing process planning and scheduling are usually considered to be two separate and distinct phases. Traditional optimization approaches to these problems do not consider the constraints of both domains simultaneously and result in suboptimal solutions. Without considering real-time machine workloads and shop floor dynamics, process plans may become suboptimal or even invalid at the time of execution. Therefore, there is a need for the integration of manufacturing process-planning and scheduling systems for generating more realistic and effective plans. After describing the complexity of the manufacturing process-planning and scheduling problems, this paper reviews the research literature on manufacturing process planning, scheduling as well as their integration, particularly on agent-based approaches to these difficult problems. Major issues in these research areas are discussed, and research opportunities and challenges are identified.     

家庭微网储能分组能量管理优化策略

在家庭能量管理系统中,可再生能源的发电功率具有不确定性和间断性,成为影响家庭能量优化调度的因素。储能系统在优化过程中过多充放电次数也会增加储能折旧费用。针对上述问题,文中提出一种储能分组能量管理优化策略。根据可再生能源出力不确定部分和确定部分为储能系统配置充电部分和调度部分。首先建立风力发电系统、光伏发电系统和储能系统模型,然后在此基础上搭建以每日用电费用最小为目标的家庭能量管理优化调度模型。最后以上海市一住宅用电为例,通过改进遗传算法对模型求解。仿真算例分析表明所提策略降低用电费用的同时可以减小可再生能源发电不确定性对能量优化调度的影响,具有一定的有效性和参考价值。     

“营改增”对物流企业的税负影响分析及税收筹划研究

物流业作为一个复合型行业, 在税收层面受到“营改增”政策的影响较大。部分物流企业因未及时应对“ 营改增”的相关变化, 导致税负增加较多, 对企业造成了较为不利的影响。为适应“营改增”的变化, 及 时优化业务流程、完善会计核算将是物流企业亟待解决的问题。     

Code Optimization Techniques in Embedded DSP Microprocessors

We address the problem of code optimization for embedded DSP microprocessors. Such processors (e.g., those in the TMS320 series) have highly irregular datapaths, and conventional code generation methods typically result in inefficient code. In this paper we formulate and solve some optimization problems that arise in code generation for processors with irregular datapaths. In addition to instruction scheduling and register allocation, we also formulate the accumulator spilling and mode selection problems that arise in DSP microprocessors. We present optimal and heuristic algorithms that determine an instruction schedule simultaneously optimizing accumulator spilling and mode selection. Experimental results are presented.