面向订单的服装企业生产计划调度

现代服装企业主要以订单的形式生产,其生产计划调度主要靠手工安排。根据服装加工企业的运作一般特点,建立了生产计划调度的数学模型,并采用遗传算法来求解,从而实现企业的生产计划安排。仿真结果表明模型和算法是可行的,满足企业的计划目标。     

基于自注意机制的供应商订单自动分配系统设计

为减少企业生产成本,进行基于自注意机制的供应商订单自动分配系统设计。将综合成本最小、生产负载均衡等因素作为约束条件,构建多目标约束模型;利用自注意机制算法获取不同因素之间的相关性,对其做数值转换,计算各约束条件的注意力值;将注意力值作为适应度值改进遗传算法,经过种群初始化、交叉变异等操作寻找最佳分配方案;将分配系统分为需求计算、供给计算、交货排程三个模块,完成自动分配系统设计。仿真实验表明系统给出的分配方案能够减少企业生产成本,缩短生产周期。     

Hadoop云平台中基于信任的访问控制模型

Hadoop云计算平台是当下最流行的云平台之一,其现有的访问控制模型采用Kerberos进行身份验证,结合基于ACL的访问授权机制,通过Delegation Token和Block Access Token等令牌,实现了该平台中简单的访问控制。该模型具有明显的缺点,即仅仅在授权时考虑了用户身份的真实性,没有考虑用户后期行为的可信性,而且权限一经授予就不再监管。提出一种适用于Hadoop云平台的基于信任的访问控制新模型——LT。LT模型基于现有的Hadoop访问控制模型,为每个用户设定信任值,通过用户在集群中的行为记录实时地更新用户信任值,并根据这个信任值动态地控制用户对平台的访问。与Hadoop平台现有的访问控制模型相比,该模型所实现的访问授权不再是一个关口控制,而是一个实时动态的过程,其粒度更细并且具有更高的安全性和灵活度。实验证明,该模型不仅正确有效,而且克服了现行Hadoop平台中访问控制安全性不足的缺点,能够动态、有效地控制用户对集群中资源的访问及使用。     

考虑动态服务能力的LSSC订单分配优化模型

物流服务供应商会根据集成商分配的不同订单价格而提供有差别的服务能力。构建新的两级物流服务供应链多目标订单分配模型,其目标函数为物流服务供应链中交易费用最小化、采购成本最小化、物流任务与供应商匹配程度最大化、总物流服务质量最大化、订单流失率以及因订单流失造成的赔付最小化的多目标订单分配优化模型。并设计一种遗传算法进行算例求解,验证模型和算法的有效性和可行性。     

CIMS分布式集成平台中的几个问题

一、问题的提出 在我们从事的863 CIMS工程分布式集成平台的设计过程中,由于单位地理位置的分散性、各部门业务的复杂性以及工程要求高等特性,因而必须深入地考虑若干重要问题。首先是系统模式的选择,其次是网络主干网的选择以及网络计算环境,第三是数据库的选择以及应用系统的开发与集成。下面介绍我们对这些问题的思考和解决方案。     

面向事件处置的信息服务集成调度模型

以电子政务、电子商务等城市应用为背景,针对这些应用中跨领域事件处置所涉及的异构、分布式资源、服务集成调度以及跨领域知识共享问题,提出了一个面向事件处置的、基于规则的信息服务集成调度模型.该集成调度模型具有4个分层结构,包括资源层(resource layer)、知识层(knowledge layer)、业务层(business layer)和表示层(representation layer).重点就知识层形式化业务规则的定义、资源和服务的引用以及规则引擎等给出了详细的设计和实现.最后,对该模型进行了验证,实现了一个城市应急联动应用中的统一接警事件处置原型系统.     

代码优化与指令调度的集成

在开发指令级并行性的编译器中,如果代码优化和指令调度各自独立进行,将导致代码优化效果的下降甚至产生副作用,文中针对这一问题,提出了代码优化和指令调度集成的思想,在此思想的基础上,介绍了一个适合于代码优化集成的指令调度算框架;并从优化的有效性、是否可逆和优化机会的产生等方面进行了分析,选出了适合集成入指令调度的传统优化种类;最后给出了这些优化的具体集成方法,该文提出的方法已经在一个指令级并行编译器上进行了实验,实验数据证明,这种优化集成方法能使优化的效果明显改善。     

钢铁企业集团订单分配模型研究

在集团统一销售的管理模式下,如何将订单在多个成员企业之间进行分配是钢铁企业集团需要研究的重要问题．为此,在对钢铁企业集团订单分配原则进行归纳总结的基础上,建立了以集团订单排产量最大和集团利润最大为目标的钢铁企业集团订单分配多目标优化模型;同时结合问题的特点,提出了模型求解的算法流程;最后,通过应用实例验证了模型和算法流程的可行性和有效性．     

云制造环境下的动态调度

在云制造环境下, 制造资源和制造能力以服务的形式封装起来, 不同的任务通过云端汇集到云平台并通过合适的调度给每个任务分配相应的服务. 由于任务在执行的过程中的不确定性, 会在某个时刻遇到突发状况从而导致对余下任务的重调度问题. 因此, 针对该问题, 考虑到云制造环境下任务的复杂性和多样性会导致在合理的时间段内很难找到最...     

云平台的任务合理化调度模型仿真分析北大核心CSCD

针对云平台的任务逐渐增加,任务调度之间的关联性逐渐被海量的任务量打破,导致任务调度的优先级也存在较强的非线性,当前的云平台任务调度模型,已经无法运用准确的约束关系确定先后顺序,造成任务请求缺失率高、资源空闲时间高和资源利用率低等弊端,提出一种模糊云平台的任务合理化调度模型,对模糊云平台任务调度问题进行了描述,分析了平台调度任务量与平均响应时间之间匹配关系,对任务优先级进行计算,得到优先级后,将其插入对应的任务队列中进行处理。分析云平台分配任务过程中平均响应时间等动态模糊特征,引入时间点概念,完成对所有时间点的处理,优先调度优先级等级高的云平台分配任务。利用任务分配优先级最大化云平台的执行任务量,最小化云平台的平均响应时间。仿真结果表明,所提模型降低了云平台平均响应时间,提高了资源的利用率。     

Multi-objective scheduling of many tasks in cloud platforms

The scheduling of a many-task workflow in a distributed computing platform is a well known NP-hard problem. The problem is even more complex and challenging when the virtualized clusters are used to execute a large number of tasks in a cloud computing platform. The difficulty lies in satisfying multiple objectives that may be of conflicting nature. For instance, it is difficult to minimize the makespan of many tasks, while reducing the resource cost and preserving the fault tolerance and/or the quality of service (QoS) at the same time. These conflicting requirements and goals are difficult to optimize due to the unknown runtime conditions, such as the availability of the resources and random workload distributions. Instead of taking a very long time to generate an optimal schedule, we propose a new method to generate suboptimal or sufficiently good schedules for smooth multitask workflows on cloud platforms.Our new multi-objective scheduling (MOS) scheme is specially tailored for clouds and based on the ordinal optimization (OO) method that was originally developed by the automation community for the design optimization of very complex dynamic systems. We extend the OO scheme to meet the special demands from cloud platforms that apply to virtual clusters of servers from multiple data centers. We prove the suboptimality through mathematical analysis. The major advantage of our MOS method lies in the significantly reduced scheduling overhead time and yet a close to optimal performance. Extensive experiments were carried out on virtual clusters with 16 to 128 virtual machines. The multitasking workflow is obtained from a real scientific LIGO workload for earth gravitational wave analysis. The experimental results show that our proposed algorithm rapidly and effectively generates a small set of semi-optimal scheduling solutions. On a 128-node virtual cluster, the method results in a thousand times of reduction in the search time for semi-optimal workflow schedules compared with the use of the Monte Carlo and the Blind Pick methods for the same purpose.     

Analyzing real cluster data for formulating allocation algorithms in cloud platforms

A problem commonly faced in Computer Science research is the lack of real usage data that can be used for the validation of algorithms. This situation is particularly true and crucial in Cloud Computing. The privacy of data managed by commercial Cloud infrastructures, together with their massive scale, makes them very uncommon to be available to the research community. Due to their scale, when designing resource allocation algorithms for Cloud infrastructures, many assumptions must be made in order to make the problem tractable.This paper provides deep analysis of a cluster data trace recently released by Google and focuses on a number of questions which have not been addressed in previous studies. In particular, we describe the characteristics of job resource usage in terms of dynamics (how it varies with time), of correlation between jobs (identify daily and/or weekly patterns), and correlation inside jobs between the different resources (dependence of memory usage on CPU usage). From this analysis, we propose a way to formalize the allocation problem on such platforms, which encompasses most job features from the trace with a small set of parameters.     

Slot allocation algorithms in centralized scheduling scheme for IEEE 802.16 based wireless mesh networks

In IEEE 802.16 based wireless mesh networks (WMNs), TDMA (Time Division Multiple Access) is employed as the channel access method and only TDD (Time Division Duplex) is supported and there are no clearly separate downlink and uplink subframes in the physical frame structure. As the uplink and downlink traffic has different characteristics in that the uplink traffic decentralizes in each MSS (Mesh Subscriber Station) and the downlink traffic centralizes in the MBS (Mesh Base Station), different scheduling methods should be taken in the uplink and downlink. This paper presents a uniform slot allocation algorithm which is suitable for both uplinks and downlinks. To achieve higher spatial reuse and greater throughput and to avoid switching frequently between receiving and transmitting within two adjacent time slots when a relay node forwards traffic, different link selection criteria are taken into account when allocating slots for uplinks and downlinks. A combined uplink and downlink slot allocation algorithm is proposed for further improving the spatial reuse and network throughput. The proposed algorithms are evaluated by extensive simulations and the results show that it has good performance in terms of spatial reuse and network throughput. To the best of the authors’ knowledge, this work is the first one that considers combined uplink and downlink slot allocation on the centralized scheduling scheme in IEEE 802.16 based WMNs.     

A bi-objective model for robust berth allocation scheduling

Berth allocation is an important port operation problem for container terminals. This paper studies how to develop a robust schedule for berth allocation that incorporates a degree of anticipation of uncertainty (e.g., vessels’ arrival time and operation time) during the schedule’s execution. This study proposes a bi-objective optimization model for minimizing cost and maximizing robustness of schedules. A heuristic is also developed for solving the bi-objective model in large-scale problem cases. Numerical experiments are conducted to validate the effectiveness and efficiency of the proposed model and method. Managerial implications are also discussed.     

Fast GA-based project scheduling for computing resources allocation in a cloud manufacturing system

Cloud manufacturing is becoming an increasingly popular enterprise model in which computing resources are made available on-demand to the user as needed. Cloud manufacturing aims at providing low-cost, resource-sharing and effective coordination. In this study, we present a genetic algorithm (GA) based resource constraint project scheduling, incorporating a number of new ideas (enhancements and local search) for solving computing resources allocation problems in a cloud manufacturing system. A newly generated offspring may not be feasible due to task precedence and resource availability constraints. Conflict resolutions and enhancements are performed on newly generated offsprings after crossover or mutation. The local search can exploit the neighborhood of solutions to find better schedules. Due to its complex characteristics, computing resources allocation in a cloud manufacturing system is NP-hard. Computational results show that the proposed GA can rapidly provide a good quality schedule that can optimally allocate computing resources and satisfy users’ demands.     

Multi-phase integrated scheduling of hybrid tasks in cloud manufacturing environment

Cloud manufacturing paradigm aims at gathering distributed manufacturing resources and enterprises to serve for more customized production. Production order which involving several tasks can be taken by distributed suppliers collaboratively at lower cost. The cloud manufacturing platform is responsible for not only arranging reasonable priorities, suitable suppliers, and production processes to multiple orders, but also scheduling hybrid tasks from different orders to manufacturing resources. To maximize the production efficiency and balance the trade-off among different production orders, this paper studies multi-phase integrated scheduling of hybrid tasks in cloud manufacturing environment, which containing order priority assignment, supplier and production process selection, and production line scheduling. Five key objectives are taken into account to analyze the interconnections among different resources and production processes. Six representative multi-objective evolutionary algorithms are adopted to solve the integrated scheduling problem. Experimental results on six production cases show that integrated scheduling is more effective than the traditional step-by-step decision, leading to less production cost and time. In addition, a comparison among the six algorithms is carried out to determine the one best suited for the integrated scheduling problem in different circumstances.     

云计算环境下的服务调度和资源调度研究

云计算中的服务调度与资源调度对云计算的性能有重要影响,在分析现有云计算调度模式的基础上,针对云计算数据密集与计算密集的特点,提出分层调度策略以实现云计算中的服务与资源调度。分层调度策略对任务进行划分确定作业优先级,并通过数据局部性和总任务完成率对资源进行分配。数值评价部分应用分层调度与已有调度进行比较。实验结果表明,所采用的调度有效提高了资源利用率,为云服务的进一步研究提供了思路。     

结合的指令调度与寄存器分配技术

提出了很多结合技术使得指令调度与寄存器分配之间进行一些信息交互,在没有引入过多溢出代码的情况下提高了指令级并行度,从而提高了性能。按照算法的特征分类介绍了几种影响力较大的算法,同时作了简单的评价和效果比较,最后介绍了有关指令调度和寄存器分配结合的一些新方向。