基于联合决策模型的物联网边缘计算资源分配

随着应用需求的增加,一些场景要求物联网能够支持密集型计算任务.传统物联网只能提供单机资源,且负载能力有限,无法有效解决时延、资源与任务的配置问题.于是提出基于联合决策模型的物联网边缘计算资源分配方法,利用边缘网络的计算优势来弥补物联网节点本地计算资源的不足,从而提高任务时延与峰值负载的性能.先从时延、能耗、计算资源和带宽资源方面进行分析,并考虑了节点移动、数据传输和卸载等情况带来的问题.根据时间和各类资源模型的分析,建立联合模型来得到资源分配调度的最佳决策,将最小卸载模型推演至最高总效用模型,并通过最速下降法对模型进行分解,在任务卸载率一定时,求解得到资源分配情况.通过动态时变物联网环境下的仿真,得到所提方法能够在较短的执行时间内,达到较高的任务完成率,且保持较低的能耗和资源分配数量.结果表明所提方法能够适应动态时变的物联网应用需求,有效完成任务与资源的卸载决策与调度分配.     

基于蝙蝠算法的云计算资源分配研究

由于云计算要面临庞大的资源分配并且具有动态性等特点,仅从单一方面来权衡资源分配策略的优劣已经不能满足需求.针对上述问题,从用户和资源提供者两个方面出发,将蝙蝠算法引入资源分配策略中,提出了以任务完成时间较短且成本最低为约束条件的调度模型.通过CloudSim平台进行模拟仿真表明,该资源分配算法能有效地兼顾完成时间和成本,在缩短任务完成时间的同时保证成本最小,提高了资源利用率.     

基于刀具刃口显微图像的附着物去除网络

准确检测并去除刀具边缘粘连的附着物是刀具显微图像豁口检测领域的一个难题，目前仍存在刀具边缘恢复不完整、附着物去除失败等问题。提出一种附着物去除的ARNet网络，采用二值掩膜引导模块区分目标与背景特征，利用去除过程的学习模块提取递归过程中的时序信息，并通过自注意力精准分离模块中的编解码结构和自注意力机制，建立附着物特征在全局特征中的依赖关系，以精准去除附着物，从而整合特征并输出无附着物图像。从实际采集的刀具刃口显微图像中裁剪含附着物区域的图像并构成数据集，在此数据集上的实验结果表明，与PReNet网络相比，该网络的峰值信噪比提高了1.016 dB，交并比IOU提升了3.48%，参数量和计算量分别减少了86.5%、90.9%，能够精确聚焦附着物区域，完整地还原刀具的真实边缘，提高了豁口检测准确率，且增强了刀具豁口高精度检测系统的稳定性和可靠性。     

卫星任务规划系统时间窗口模块的设计与实现

设计并实现了卫星任务规划系统的时间窗口模块。通过数据接口设置任务的约束要求,利用STK(Satellite Tool Kit)的时间窗口计算能力,计算卫星传感器对于地面目标的可用时间窗口,以简化资源调度模块的求解过程。对于没有可用时间窗口的任务,时间窗口模块能分析时间窗口分配失败的原因,用户可以根据失败原因来分析卫星系统的动态能力。     

多模式自动化存取系统设备调度与任务分配

多模式自动化存取系统是一种新型仓储系统,其存储空间利用率高、设备配置灵活.为提高作业效率,降低运行成本,需对系统进行设备调度和任务分配,实现按需配置和管理系统资源.通过将系统的作业过程建模为多个服务中心组成的排队网络,对服务中心设备数量进行调度,保证系统稳态运行.基于预期作业时间设计具有两级模糊规则的模糊控制器,为托盘物资上下架任务分配托盘提升机,实现托盘物资上下架任务的优化分配,减少作业过程所需的运输路径,从而提高作业效率,减少作业叉车数量,降低作业成本.文中通过一个托盘物资上架任务仿真算例,验证了模糊控制任务分配策略的有效性.     

基于深度优先贪婪搜索的可重构硬件任务划分算法

针对可重构计算硬件任务划分通信成本较小化的问题,提出了一种基于深度优先贪婪搜索划分(DFGSP)算法。首先,从待调度的就绪队列中取出队首任务,在某一硬件面积约束下,按深度优先搜索(DFS)方式扫描一个计算密集型任务转换来的有向无环图(DAG),逐个划入满足要求的节点;然后,一遇到不满足面积要求的任务节点时,就计算当前划分模块间输出边数(可量化为通信成本);最后,跳过当前不满足要求的任务节点,继续搜索该点之后处于就绪状态的节点,当搜索到满足要求的点时,按加入该点后不增加当前划分块间输出边数和尽可能填满可重构运算阵列的原则进行。实验结果表明,与现有的簇划分(CBP)、簇层次敏感两种划分算法相比,提出的算法获得了最小划分模块数和平均跨模块间I/O边数最小的均值,通过实际验证,算法显著地改善了硬件任务的划分效果,而且运行开销没有明显增加。     

时间敏感网络业务流动态调度场景中的批量重配置算法

工业自动化领域广泛使用时间敏感网络技术. 该领域业务流的调度方式主要包含静态调度和动态调度. 静态调度一次计算所有业务流, 可以最大程度节省链路和时间资源, 但是计算时间长, 无法灵活处理新增业务流. 动态调度以增量的形式计算新增业务流, 计算时间短, 但是资源分配不够合理, 会产生时隙碎片. 全局流重配置机制可以定期对网络中所有业务流进行重新规划, 来优化链路和时间资源的分配, 但该机制只适用于拥有较少业务流的小型网络, 业务流数量的增多会引起计算时间的急剧增长, 影响后续到来的业务流. 本文在现有动态调度算法的基础上, 设计了批量重配置算法. 该算法给出了新的评价指标——网络吞吐率, 并在满足动态调度秒级响应时间的情况下, 定期重配置网络中的部分业务流, 优化网络资源配置. 此外, 算法给出了重配置业务流的选取标准, 并优化了流的路径选择标准和传输开始时间计算方式. 本文针对原算法和增加了批量重配置机制的改进算法进行了仿真实验, 实验结果表明, 改进算法可以在拥有数千条业务流的大型网络运行, 并在网络吞吐率和调度成功的流数量方面有16.5%和5.5%的提升, 同时保证了算法的秒级计算时间.     

基于有向无环图的成本-时间优化调度算法

网格计算环境下,基于有向无环图(DAG)的成本-时间优化调度算法运用经济规律把网格用户的任务映射到网格资源中运行.OGS算法考虑了任务间的优先关系,使得任务完成时间最小,但没考虑到在网格环境中所需的成本.Nimrod/G模型中提出基于时间和成本限制下的优化调度算法(DBC)考虑了时间和成本,但没考虑任务问的优先关系.本文综合考虑了成本-时间因素以及任务间的优先关系,在不增加完成时间的基础上,把任务映射到价格便宜的机器上,提出了基于有向无环图的成本-时间优化调度算法.通过仿真表明,相对OGS算法,该算法减少了所需成本.     

基于有向无环图的成本-时间优化调度算法

网格计算环境下,基于有向无环图（DAG）的成本-时间优化调度算法运用经济规律把网格用户的任务映射到网格资源中运行。OGS算法考虑了任务间的优先关系,使得任务完成时间最小,但没考虑到在网格环境中所需的成本。Nimrod/G模型中提出基于时间和成本限制下的优化调度算法（DBC）考虑了时间和成本,但没考虑任务间的优先关系。本文综合考虑了成本-时间因素以及任务间的优先关系,在不增加完成时间的基础上,把任务映射到价格便宜的机器上,提出了基于有向无环图的成本-时间优化调度算法。通过仿真表明,相对OGS算法,该算法减少了所需成本。     

基于C／S模式的垂直提升式刀具库管理信息系统

在一些大型的机械加工中心,用于数控加工的刀具和零件种类多数量大。为了科学管理和提高效率,设计开发了基于C／S模式的垂直提升式刀具库管理信息系统,并将多个独立刀具库接入到企业内部网中。在对系统功能分析的基础上,重点介绍了系统的总体设计、结构模块、主控模块、数据库软件设计。     

Sequencing optimisation for makespan improvement at wet-etch tools

The complex nature of wet-etch tools and their peculiar scheduling constraints pose a relevant challenge for the development and implementation of makespan optimisation strategies, especially when rigid scheduling rules have to be considered. In this paper, an optimisation model is developed for sequencing of wafer batches outside a wet-etch tool and scheduling of tool-internal handler moves. The scheduling algorithm is inspired by the control logics governing wet-etch tools operating in a real semiconductor manufacturing plant and proves effective in generating efficient and detailed schedules in short computational times. The mathematical formulation developed for the scheduling problem is based on generic and realistic assumptions for both the job flow and the material handling system. The sequencing module combines an exact optimisation approach, based on an efficient permutation concept, and a heuristics optimisation approach, based on genetic algorithms. The results obtained show that significant makespan reductions can be obtained by means of a mere sequencing optimisation. Using this optimisation strategy, variations to the scheduling logics, that are generally more difficult and expensive to implement, are avoided. A sensitivity analysis on genetic algorithm operators is also conducted and considerations on the best performing selection, cross-over and mutation operators are presented.     

基于遗传算法的作业车间调度优化

将遗传算法和启发式调度规则相结合，研究了具有柔性加工路径的作业车间的智能优 化调度问题，调度规则的引入使该算法具有较高的搜索效率，遗传算法的引入保证了解的全 局最优性，对照算例，表明该算法在求解性能和效率两方面均具有显著的优势．     

单位处理时间的多处理机任务调度近似算法

研究多处理机任务调度模型P_m|fix,p_j=1|C_max,即在m个处理机系统中调度n个时间长度都为1的多处理机任务,每个任务指派到所需一组处理机上不可剥夺地执行。其更一般的问题是P_m|fix|C_max,在网络并行计算、多播系统及工程规划等领域都有广泛的应用。该问题早已证明为NP难问题,而且也不存在常数近似算法。基于部分调度和宽度优先原则构造了该问题的一个多项式时间近似算法,并从理论上证明了该算法在最坏情况下的近似比为2m+1,优于已有文献中2m的目前最好结果。     

Scheduling unit length jobs on parallel machines with lookahead information

This paper studies two closely related online-list scheduling problems of a set of n jobs with unit processing times on a set of m multipurpose machines. It is assumed that there are k different job types, where each job type can be processed on a unique subset of machines. In the classical definition of online-list scheduling, the scheduler has all the information about the next job to be scheduled in the list while there is uncertainty about all the other jobs in the list not yet scheduled. We extend this classical definition to include lookahead abilities, i.e., at each decision point, in addition to the information about the next job in the list, the scheduler has all the information about the next h jobs beyond the current one in the list. We show that for the problem of minimizing the makespan there exists an optimal (1-competitive) algorithm for the online problem when there are two job types. That is, the online algorithm gives the same minimal makespan as the optimal offline algorithm for any instance of the problem. Furthermore, we show that for more than two job types no such online algorithm exists. We also develop several dynamic programming algorithms to solve a stochastic version of the problem, where the probability distribution of the job types is known and the objective is to minimize the expected makespan.     

一种改进型遗传算法在FMS刀具调度中的应用

在柔性制造系统（FMS）中,刀具调度问题关系着刀具的利用率和整个FMS的生产效率,如何对众多刀具进行合理的调度已成为当前FMS中的一项关键技术。在对遗传算法进行改进的基础上建立了在有限刀具资源约束下的FMS刀具调度优化模型,得出针对刀具交换次数的目标函数,通过比较与实验验证了这种算法的有效性和优越性。     

Integration of process planning and scheduling using chaotic particle swarm optimization algorithm

Process planning and scheduling are two of the most important manufacturing functions traditionally performed separately and sequentially. These functions being complementary and interrelated, their integration is essential for the optimal utilization of manufacturing resources. Such integration is also significant for improving the performance of the modern manufacturing system. A variety of alternative manufacturing resources (machine tools, cutting tools, tool access directions, etc.) causes integrated process planning and scheduling (IPPS) problem to be strongly NP-hard (non deterministic polynomial) in terms of combinatorial optimization. Therefore, an optimal solution for the problem is searched in a vast search space. In order to explore the search space comprehensively and avoid being trapped into local optima, this paper focuses on using the method based on the particle swarm optimization algorithm and chaos theory (cPSO). The initial solutions for the IPPS problem are presented in the form of the particles of cPSO algorithm. The particle encoding/decoding scheme is also proposed in this paper. Flexible process and scheduling plans are presented using AND/OR network and five flexibility types: machine, tool, tool access direction (TAD), process, and sequence flexibility. Optimal process plans are obtained by multi-objective optimization of production time and production cost. On the other hand, optimal scheduling plans are generated based on three objective functions: makespan, balanced level of machine utilization, and mean flow time. The proposed cPSO algorithm is implemented in Matlab environment and verified extensively using five experimental studies. The experimental results show that the proposed algorithm outperforms genetic algorithm (GA), simulated annealing (SA) based approach, and hybrid algorithm. Moreover, the scheduling plans obtained by the proposed methodology are additionally tested by Khepera II mobile robot using a laboratory model of manufacturing environment.     

多阶段混合Flow Shop调度问题及其遗传求解算法

针对多阶段混合Ｆｌｏｗ Ｓｈｏｐ 调度问题的一般结构和不同的调度目标函数,提出混合整数规划模型,并基于问题的结构特点设计了遗传求解算法。计算实验结果表明,遗传算法对于不同规模和结构的问题具有良好的适应性和求解性能     

特殊工艺约束下最小完工时间调度问题的新并行遗传算法

针对在特殊工艺约束下非等同并行机最小完工时间调度问题,设计了一个基于向量组编码的新的遗传算法。此算法的编码方法简单,能有效地反映实际调度方案,并能保证交叉和变异后染色体满足约束条件,收敛速度快。同时为更好地适应调度实时性和解决大型企业此类问题的需要,在基于遗传算法自然并行性特点的基础上,实现了主从式控制网络模式下并行遗传算法。仿真结果表明,此算法是有效的,优于普通的遗传算法,具有较高的并行性。     

Scheduling two parallel machines with machine-dependent availabilities

A two-parallel-machine scheduling problem with machine-dependent availabilities where one machine is subject to tool changes and the other is subject to periodic maintenance is considered. The objective is to determine the start time of each tool change activity and schedule all the jobs to the two machines such that the makespan is minimized. Due to the NP-hardness of the non-approximability of the problem, there does not exist any polynomial time approximation algorithm with a worst-case ratio less than 2 for the problem unless P=NP. To solve small sized and moderate sized instances of the problem, a mixed 0–1 programming model is proposed. To solve large sized instances of the problem, nine heuristic algorithms that employ two classical dispatching rules, two assignment mechanisms and a post-optimization procedure are proposed. To evaluate the performance of the algorithms, some machine setting featured lower bounds are provided. Computational experiment shows that the average-case relative error ratio of the heuristic algorithm that based on the classical LPT rule, two assignment procedures and a post-optimization procedure is less than 8%, which implies that it is promising for problem and suitable for real-world application.     

模块化思想在工装快速准备中的研究应用

面对某一特定的生产任务时,企业如何在时间、成本、质量、服务（TCQS）方面快速协调不同加工任务,快速准备工装满足工装需求已经成为我国航天制造企业大规模定制生产中不容忽视的关键问题之一。从工装模块化设计的方法出发,提出了适合工装模块划分和编码的原则,并以夹具为例说明模块编码的体系结构,为企业在工装快速准备方面提供了方向和思路。