面向网络化制造的装备资源多目标优选

本文分析了网络化制造环境下装备资源的特点,对区域装备资源优选中的多目标问题进行了分析,建立了多目标问题的数学模型,提出了多目标准则的标准化处理方法,并以此模型为基础,采用最优权重法建立了装备资源优选的总目标,建立了基于AHP算法的装备资源优选模型,为用户提供了基于资源加工能力、加工时间等多种方案的资源优选算法,使系统用户能够有效地查找和使用各种装备资源.     

Beam-PSO优化算法在多行程车辆路径问题的应用

针对城市物流配送系统，研究了一类带时间窗和释放时间约束的多行程车辆路径问题。首先，对该运输调度问题进行了描述，构建了以总配送时长最小化为目标的数学模型。其次，为了快速获得问题的满意解，提出了Beam-PSO优化算法。在算法设计中，结合该问题的性质，构建了基于随机键的编解码方法，以克服标准粒子群算法无法直接适用于求解离散问题的不足。同时，设计了基于Beam search优化技术的局部搜索流程，用于强化算法的优化性能。最后，进行了仿真实验，实验结果表明了Beam-PSO优化算法的可行性和有效性。     

多灾害点应急资源调度研究与实现

针对多灾害点、多点出救、多目标应急调度问题,建立一种以时间最短、成本最低为目标的数学模型。考虑到调度过程中各灾害点对资源的竞争,提出利用表上作业算法对该模型进行优化求解,实现了在整体的优化方案中时间和成本总体消耗最少。提出的方法简单、实用、易用,并成功应用在省级应急管理信息示范平台上。最后,通过一个实例验证该方法的有效性。     

多AGV和机器集成的多目标柔性作业车间调度研究

为解决智能制造环境中具有多时间和多AGV约束的柔性作业车间调度问题,构建了以最小化最大完工时间、最小化总延期、最小化设备总负荷为目标的机器/AGV双约束多目标调度模型,模型中综合考虑加工时间、工件到达时间、交货期等多时间因素,进行了多AGV和机器集成调度。为求解该模型,设计了新的AGV调度规则和改进的NSGA-算法,算法中提出了基于工序的扩展染色体编码方式和基于AGV分配的贪婪式解码策略,同时设计了不同参数控制的多种群二元锦标赛选择和分段交叉变异策略以及基于Pareto级的去重精英保留策略,以促进个体协同优化搜索。通过实例实验,分析了不同AGV数量任务分配方案下的模型有效性,对4个案例的仿真测试和同类算法比较解也验证了改进NSGA-算法求解该模型的有效性。     

分支定界法在制丝多线排产优化中的应用

为优化制丝多生产线排产,设计了带约束限制、以总生产成本为优化目标的排产数学模型.该模型使用分支界定优化算法进行求解,并针对实际的制丝任务排产,改进了算法流程和求解策略.采用.NET编程实现了制丝多线生产的整数规划优化.通过制丝生产任务分配实例,验证了分支定界算法的有效性.该算法在满足实际生产约束条件下,获得了优化的制丝多线任务分配方案,从而降低了制丝生产总成本和烟丝库存.     

基于NSGA2算法的并行机多目标调度问题研究

针对并行机多目标调度问题,以完工时间和总延迟时间最小为目标函数建立了数学模型,从而将具有解决复杂组合优化问题的非劣排序遗传算法NSGA2应用于求解多目标并行机调度问题。文中详细描述了用NSGA2算法求解并行机调度问题的步骤,并通过Matlab仿真,表明YhqNSGA2算法求解多目标并行机调度问题的可行性和有效性。     

基于混合粒子群算法的试验选址问题研究

大型海上试验涉及分散在全国各地的人员、平台、测量设备和产品等资源。试验海区分布在漫长的海岸线上，将这些资源运输到合适的海区属于运输问题。试验海区的选择会影响资源的取舍，资源的取舍与试验流程优化这一车间调度问题密切相关，而试验流程优化反过来又会影响资源的取舍和海区的选择。因此，试验海区的选择是运输问题和车
车间调度问题的耦合。本文建立了该问题的数学模型，并分别用粒子群算法和排队论处理流程优化中的时间约束和资源约束，再用启发式算法对运输问题进行优化。最后，以某产品的试验为例对算法进行了验证，结果表明了该方法的有效性。     

基于遗传算法的多技能需求现场产品服务调度

多技能需求的现场产品服务调度结合了多旅行商问题与多技能项目调度问题,需综合考虑路径优化与技能匹配。针对该问题,考虑时间窗因素,以最短旅途时间和最少客户等待时间为目标建立数学模型,基于分段染色体编码的遗传算法并采用成组分段交叉算子进行求解。实例结果证明,该算法的解能避免过早收敛,有较高的搜索效率。     

基于人工免疫算法的航空多项目资源均衡技术

为解决航空企业内部多个制造项目并行情况下的资源均衡问题,引入了一种适合求解并行项目资源均衡问题的数学模型,该模型可以有效地将并行多项目资源均衡问题转化为单项目资源均衡问题;并提出了一种改进型的人工免疫算法;该算法通过引入自适应高变异算子与遗传操作的混合模式,能够根据抗体的亲和度调整变异步长,来达到以较快速度完成给定范围搜索的目的。最后,自主开发航空项目管理软件,对某型飞机机身部件的两个并行的装配项目进行了实例仿真,验证了该算法的有效性。     

多机场地面等待策略数学模型的研究

为有效地解决空中交通拥挤问题,研究了空中交通流量管理方法中的地面等待策略。通过地面等待来调节空中交通网络的流量,减少延误时间,从而减少经济损失,提高机场和空域资源的利用率。在空中交通流量管理的各种方法中,地面等待策略是一种比较有效的方法。针对多机场地面等待问题,提出一种以地面等待成本和延迟成本为目标函数的方法,并建立相应的数学模型,为实际的流量管理提供理论方法和依据。通过对机场数据的仿真计算,证明了该模型的有效性和可行性。     

基于任务复制的多维QoS云计算任务调度

为了满足云计算环境下用户服务质量(QoS)需求和提高虚拟资源空闲时间段的利用率,提出了一种基于任务复制的多维QoS任务调度策略。首先,构建云资源模型和用户QoS模型,然后根据虚拟资源的利用情况和QoS的满意度对虚拟机进行性能测评,选择综合性能更高的虚拟资源进行任务的分配;在任务执行时为了缩短任务的完成时间,在调度过程中引入了在空闲时间段复制父任务的方式。通过仿真实验将该算法与HEFT、CPOP进行比较,实验结果显示：当用户偏好可靠性执行时,该算法平均可靠性比HEFT和CPOP高;当用户偏好完成时间和费用花费执行时,该算法平均完成时间比HEFT和CPOP少;当用户无偏好执行时,该算法平均完成时间和平均花费均比HEFT和CPOP少。结果表明该算法能有效提高资源利用率和用户的满意度。     

A Load Balanced Task Scheduling Heuristic for Large-Scale Computing Systems

Optimal task allocation in Large-Scale Computing Systems (LSCSs) that endeavors to balance the load across limited computing resources is considered an NP-hard problem. MinMin algorithm is one of the most widely used heuristic for scheduling tasks on limited computing resources. The MinMin minimizes makespan compared to other algorithms, such as Heterogeneous Earliest Finish Time (HEFT), duplication based algorithms, and clustering algorithms. However, MinMin results in unbalanced utilization of resources especially when majority of tasks have lower computational requirements. In this work we consider a computational model where each machine has certain bounded capacity to execute a predefined number of tasks simultaneously. Based on aforementioned model, a task scheduling heuristic Extended High to Low Load (ExH2LL) is proposed that attempts to balance the workload across the available computing resources while improving the resource utilization and reducing the makespan. ExH2LL dynamically identifies task-to-machine assignment considering the existing load on all machines. We compare ExH2LL with MinMin, H2LL, Improved MinMin Task Scheduling (IMMTS), Load Balanced MaxMin (LBM), and M-Level Suffrage-Based Scheduling Algorithm (MSSA). Simulation results show that ExH2LL outperforms the compared heuristics with respect to makespan and resource utilization. Moreover, we formally model and verify the working of ExH2LL using High Level Petri Nets, Satisfiability Modulo Theories Library, and Z3 Solver.     

A resource-aware scheduling algorithm with reduced task duplication on heterogeneous computing systems

To satisfy the high-performance requirements of application executions, many kinds of task scheduling algorithms have been proposed. Among them, duplication-based scheduling algorithms achieve higher performance compared to others. However, because of their greedy feature, they duplicate parents of each task as long as the finish time can be reduced, which leads to a superfluous consumption of resource. However, a large amount of duplications are unnecessary because slight delay of some uncritical tasks does not affect the overall makespan. Moreover, these redundant duplications would occupy the resources, delay the execution of subsequent tasks, and increase the schedule makespan consequently. In this paper, we propose a novel duplication-based algorithm designed to overcome the above drawbacks. The proposed algorithm is to schedule tasks with the least redundant duplications. An optimizing scheme is introduced to search and remove redundancy for a schedule generated by the proposed algorithm further. Randomly generated directed acyclic graphs and two real-world applications are tested in our experiments. Experimental results show that the proposed algorithm can save up to 15.59  % resource consumption compared with the other algorithms. The makespan has improvement as well.     

基于任务复制的网格任务调度算法

网格中资源之间存在着通信延迟，通过任务复制的冗余，可以减少任务之间的通信开销，缩短整个计算程序的计算时间。目前网格中的任务调度算法基本上是没有考虑任务复制的；而基于任务复制调度算法往往会产生过多的复制任务，增大系统开销，甚至有可能延迟计算时间。由于基于任务复制的任务调度是一个NP问题，因此本文提出了一种基于任务复制的网格资源调度算法，以减少调度长度为主要目标、减少任务复制量和资源占用量为次要目标。该算法在调度长度和任务复制数量以及占用资源数量方面都等于或优于其它算法。     

On the efficiency of several VM provisioning strategies for workflows with multi-threaded tasks on clouds

Cloud computing promises the delivery of on-demand pay-per-use access to unlimited resources. Using these resources requires more than a simple access to them as most clients have certain constraints in terms of cost and time that need to be fulfilled. Therefore certain scheduling heuristics have been devised to optimize the placement of client tasks on allocated virtual machines. The applications can be roughly divided in two categories: independent bag-of-tasks and workflows. In this paper we focus on the latter and investigate a less studied problem, i.e., the effect the virtual machine allocation policy has on the scheduling outcome. For this we look at how workflow structure, execution time, virtual machine instance type affect the efficiency of the provisioning method when cost and makespan are considered. To aid our study we devised a mathematical model for cost and makespan in case single or multiple instance types are used. While the model allows us to determine the boundaries for two of our extreme methods, the complexity of workflow applications calls for a more experimental approach to determine the general relation. For this purpose we considered synthetically generated workflows that cover a wide range of possible cases. Results have shown the need for probabilistic selection methods in case small and heterogeneous execution times are used, while for large homogeneous ones the best algorithm is clearly noticed. Several other conclusions regarding the efficiency of powerful instance types as compared to weaker ones, and of dynamic methods against static ones are also made.     

网格任务调度方法研究

网格计算中的关键问题之一是计算任务在各个资源之间的调度。提出了基于量子遗传算法（QGA）的网格任务调度算法,以减少调度时间为主要目标,增加资源利用率为次要目标。该算法采用量子比特间接编码的方式,通过有向无环图（DAG）来描述子任务间的依赖关系,根据深度值来给子任务的执行顺序进行排序。仿真结果显示,无论是任务完成时间还是资源利用率,此方法都明显优于基于遗传算法（GA）的网格调度算法。     

An Integrated Approach to Locality-Conscious Processor Allocation and Scheduling of Mixed-Parallel Applications

Complex parallel applications can often be modeled as directed acyclic graphs of coarse-grained application tasks with dependences. These applications exhibit both task and data parallelism, and combining these two (also called mixed parallelism) has been shown to be an effective model for their execution. In this paper, we present an algorithm to compute the appropriate mix of task and data parallelism required to minimize the parallel completion time (makespan) of these applications. In other words, our algorithm determines the set of tasks that should be run concurrently and the number of processors to be allocated to each task. The processor allocation and scheduling decisions are made in an integrated manner and are based on several factors such as the structure of the task graph, the runtime estimates and scalability characteristics of the tasks, and the intertask data communication volumes. A locality-conscious scheduling strategy is used to improve intertask data reuse. Evaluation through simulations and actual executions of task graphs derived from real applications and synthetic graphs shows that our algorithm consistently generates schedules with a lower makespan as compared to Critical Path Reduction (CPR) and Critical Path and Allocation (CPA), two previously proposed scheduling algorithms. Our algorithm also produces schedules that have a lower makespan than pure task- and data-parallel schedules. For task graphs with known optimal schedules or lower bounds on the makespan, our algorithm generates schedules that are closer to the optima than other scheduling approaches.     

云环境中基于分解的多目标工作流调度算法

云服务提供商在给用户提供海量虚拟资源的同时,也面临着一个现实的问题,即怎样调度这些资源,以最小的代价(完工时间、执行费用、资源利用率等)完成工作流的执行。针对IaaS环境下的工作流调度问题,以完工时间和执行费用作为目标,提出了一种基于分解的多目标工作流调度算法。该算法结合了基于列表的启发式算法和多目标进化算法的选择过程,采用一种分解方法,将多目标优化问题分解为一组单目标优化子问题,然后同时求解这些单目标子问题,使得调度过程更为简单有效。算法利用天马项目发布的现实世界中的工作流进行实验,结果表明,和MOHEFT算法以及NSGA-II*算法相比较,所提出的算法能得到更优的Pareto解集,同时具有更低的时间复杂度。     

CLPS-GA: A case library and Pareto solution-based hybrid genetic algorithm for energy-aware cloud service scheduling

Since the appearance of cloud computing, computing capacity has been charged as a service through the network. The optimal scheduling of computing resources (OSCR) over the network is a core part for a cloud service center. With the coming of virtualization, the OSCR problem has become more complex than ever. Previous work, either on model building or scheduling algorithms, can no longer offer us a satisfactory resolution. In this paper, a more comprehensive and accurate model for OSCR is formulated. In this model, the cloud computing environment is considered to be highly heterogeneous with processors of uncertain loading information. Along with makespan, the energy consumption is considered as one of the optimization objectives from both economic and ecological perspectives. To provide more attentive services, the model seeks to find Pareto solutions for this bi-objective optimization problem. On the basis of classic multi-objective genetic algorithm, a case library and Pareto solution based hybrid Genetic Algorithm (CLPS-GA) is proposed to solve the model. The major components of CLPS-GA include a multi-parent crossover operator (MPCO), a two-stage algorithm structure, and a case library. Experimental results have verified the effectiveness of CLPS-GA in terms of convergence, stability, and solution diversity.     

不确定环境下项目拆分和带缓冲时间的多项目协同调度研究

针对不确定环境下移动式装配的项目存在项目工期随机延长的问题,首先引用项目拆分思想,将单项目虚拟拆分成多项目;在加入最大鲁棒性约束下,以最小化项目工期为目标建立数学优化模型。并提出了改进的两阶段循环算法求解：项目划分阶段通过子项目拆分算法进行子项目划分;项目调度阶段以布谷鸟算法为框架对划分后的多项目调度进行求解,并将调度结果反馈至上阶段。最后选取PSPLIB算例库中不同规模的算例,分析各种参数在不同规模下对项目计划的影响。实例验证结果表明,所提方法能在不确定环境下提高项目资源利用率并缩短工期。