Integrated process planning and scheduling in a supply chain

This paper deals with the integration of process planning and scheduling, which is one of the most important functions in a supply chain to achieve high quality products at lower cost, lower inventory, and high level of performance. Solving the problem is essential for the generation of flexible process sequences with resource selection and for the decision of the operation schedules that can minimize makespan. We formulate a mixed integer programming model to solve this problem of integration. This model considers alternative resources: sequences and precedence constraints. To solve the model, we develop a new evolutionary search approach based on a topological sort. We use the topological sort to generate a set of feasible sequences in the model within a reasonable computing time. Since precedence constraints between operations are handled by the topological sort, the developed evolutionary search approach produces only feasible solutions. The experimental results using various sizes of problems provide a way to demonstrate the efficiency of the developed evolutionary search approach.     

基于标记因果顺序挖掘的多标记分类方法

在多标记学习(MLL)问题中,每个示例都与一组标记相关联.为了实现对未见示例的高效预测,挖掘和利用标记之间的关系是至关重要的.大多数已有的研究都将关系简化为标记之间的相关性,而相关性又通常基于标记的共现性.揭示了因果关系对于描述一个标记在学习过程中如何帮助另一个标记更为重要.基于这一观察,提出了两种策略来从标记因果有向...     

VLCC中的DAG并行算法

基于组件的密码学虚拟实验室（VLCC）采用有向无环图（DAG）的拓扑排序机制管理组件。在分析VLCC各组件之间的数据依赖和运行次序关系的基础上,提出一种新的基于Java多线程机制和“唤醒”机制的DAG并行算法。与拓扑排序算法相比,具有低算法时间复杂度的特点。实验结果表明,系统在新算法下较大地缩短了系统运行时间,提高资源使用效率和用户满意度,能更好地完善VLCC。     

一种新的基于邻接矩阵的拓扑排序算法

为了降低基于邻接矩阵的拓扑排序算法的复杂性，将单顶点算法框架扩展成集合算法框架，给出一些便于进行拓扑排序的有向无环图的性质。在此基础上，定义了适合进行弧删除操作和无前驱顶点判断的邻接矩阵运算，给出了有向弧邻接矩阵的存储方案，最终提出了一种时间和空间复杂度都比较低的拓扑排序算法。     

云计算环境下多有向无环图工作流的节能调度算法

针对多有向无环图(DAG)工作流节能调度算法中存在的节能效果不佳、适用范围较窄和无法兼顾性能优化等问题,提出了一种新的多DAG工作流节能调度方法--MREO。MREO在对计算密集型和通信密集型任务特点进行分析的基础上,通过整合独立任务,减少了处理器的数量,并利用回溯和分支限界算法对任务整合路径进行动态的优化选择,有效降低了整合算法的复杂度。实验结果证明,MREO在保证多DAG工作流性能的前提下,能够有效降低系统的计算和通信能量开销,获得了良好的节能效果。     

Merged Multiresolution Hierarchies for Shadow Map Compression

Multiresolution Hierarchies (MH) and Directed Acyclic Graphs (DAG) are two recent approaches for the compression of high‐resolution shadow information. In this paper, we introduce Merged Multiresolution Hierarchies (MMH), a novel data structure that unifies both concepts. An MMH leverages both hierarchical homogeneity exploited in MHs, as well as topological similarities exploited in DAG representations. We propose an efficient hash‐based technique to quickly identify and remove redundant subtree instances in a modified relative MH representation. Our solution remains lossless and significantly improves the compression rate compared to both preceding shadow map compression algorithms, while retaining the full run‐time performance of traditional MH representations.     

PLC梯形图的广义表转换

提出了利用串并联归并算法以实现PLC梯形图到指令表的转换方法。该算法将梯形图转化为有向无环图,对图中的串并联关系进行分类归并,将串并联结构按层次存储在广义表中,根据广义表生成指令表。该算法克服了传统拓扑排序算法在梯形图结构复杂时产生误判的缺陷,增加了检查逻辑错误的功能。在最佳情况下,该算法的时间复杂度为O(n),最差情况下为O(n2),与拓扑排序算法基本一致,有时略优于拓扑排序算法。     

Indexing hierarchical structures using graph spectra

Hierarchical image structures are abundant in computer vision and have been used to encode part structure, scale spaces, and a variety of multiresolution features. In this paper, we describe a framework for indexing such representations that embeds the topological structure of a directed acyclic graph (DAG) into a low-dimensional vector space. Based on a novel spectral characterization of a DAG, this topological signature allows us to efficiently retrieve a promising set of candidates from a database of models using a simple nearest-neighbor search. We establish the insensitivity of the signature to minor perturbation of graph structure due to noise, occlusion, or node split/merge. To accommodate large-scale occlusion, the DAG rooted at each nonleaf node of the query "votes" for model objects that share that "part," effectively accumulating local evidence in a model DAG's topological subspaces. We demonstrate the approach with a series of indexing experiments in the domain of view-based 3D object recognition using shock graphs.     

处理顺序约束的信息物理融合系统静态任务表调度算法

针对异构环境并行计算的静态任务调度问题,以最小化有向无环图 (Directed acyclic graph, DAG)的执行跨度为目标,改变HEFT (Heterogeneous earliest finish time)算法中任务上行权重的计算方法, 获得更加合理的任务顺序排列,提出了一种最早完成时间优先的表调度算法IHEFT (Improvement heterogeneous earliest finish time).该算法在计算任务的上行权重时, 分别计算该任务分配给不同资源的上行权重,取其最小值,比使用所有资源对该任务的平均处理时间进行计算的HEFT算法更为准确. 确定任务的处理顺序后采用最早完成时间越小越优先的策略将任务分配给最优资源,并使得任务的开始执行时间和结束时间满足DAG中有向边的通讯时间约束.通过使用部分文献中的算例数据以及随机生成满足一定结构要求的DAG进行算法测试,将IHEFT与HEFT, CPOP (Critical-path-on-a-processor)和LDCP (Longest dynamic critical path)进行了比较,结果显示IHEFT算法更有效,而且时间复杂度较低.     

基于HEFT和CPOP的相关任务表调度算法

DAG任务调度是当前研究的热点,DAG任务模型中任务的调度顺序一方面会影响用户服务满意质量,另一方面也会影响云服务资源的利用率,高效的任务调度算法能够使多核处理器的资源分配和并行计算能力更强.表调度算法HEFT算法以及CPOP算法在相关任务调度中存在效率较低等问题.本文基于HEFT算法和CPOP算法,提出了一种相关任务调度模型和相关任务调度算法IHEFT算法,对任务排序和任务调度两个方面进行改进.任务排序阶段,以任务的方差以及平均通信代价作为排序的依据;任务调度阶段,对满足任务复制条件的结点进行任务复制.实验证明,IHEFT算法在任务调度跨度、任务调度平均等待时间以及平均Slack值方面均优于HEFT算法和CPOP算法.     

基于DAG的拓扑排序改进算法及在BOM中的运用

文章讨论了传统的BOM防止嵌套错误算法和低层码计算的算法的实现过程。在分析算法的实现过程后对其原理进行评价的基础上,将BOM树结构和DAG图性质进行比较后对这两种算法进行改进,提出了一种蕴涵了拓扑排序思想的算法。最后编程实现了此算法的伪代码,该算法在实际运用中取得了明显的效果,减少了数据库系统资源的占用,大大提高了数据库的性能和响应能力。     

Analysis of a Heuristic for Code Partitioning

In this paper, we analyze the time complexity and performance of a heuristic for code partitioning for Distributed Memory Multiprocessors (DMMs). The partitioning method is data-flow based where all levels of parallelism are exploited. Given a weighted Directed Acyclic Graph (DAG) representation of the program, our algorithm automatically determines the granularity of parallelism by partitioning the graph into tasks to be scheduled on the DMM. The granularity of parallelism depends only on the program to be executed and on the target machine parameters. The output of our algorithm is passed on as input to the scheduling phase. Finding an optimal solution to this problem is NP-complete. Due to the high cost of graph algorithms, it is nearly impossible to come up with close to optimal solutions that do not have very high cost (higher order polynomial). Our proposed heuristic gives good performance and has relatively low cost.     

Particle swarm optimization with adaptive time-varying topology connectivity

Particle swarm optimization (PSO) has shown its competitive performance for solving benchmark and real-world optimization problems. Nevertheless, it requires better control of exploration/exploitation searches to prevent the premature convergence of swarms. Thus, this paper proposes a new PSO variant called PSO with adaptive time-varying topology connectivity (PSO-ATVTC) that employs an ATVTC module and a new learning framework. The proposed ATVTC module specifically aims to balance the algorithm's exploration/exploitation searches by varying the particle's topology connectivity with time according to its searching performance. The proposed learning framework consists of a new velocity update mechanism and a new neighborhood search operator to improve the algorithm's performance. A comprehensive study was conducted on 24 benchmark functions and one real-world problem. Compared with nine well-established PSO variants and six other cutting-edge metaheuristic search algorithms, the searching performance of PSO-ATVTC was proven to be more prominent in majority of the tested problems.     

DAG图拓扑排序的动画实验模型设计

传统抽象的程序演示实验难以体现数据结构的本质.本文通过比较传统程序实验和具体化的多媒体动画实验,逐步演示抽象复杂的DAG图拓扑排序实现步骤,为掌握数据结构及其它课程提供直观有效的实验参考模型.     

面向修复的计算技术分析

面向修复计算把硬件故障、软件错误和操作员失误当作不可避免的事实,研究的是面对事实的处理技术。它着眼于通过降低MTTR而提高系统的可用性,同时减少系统的总拥有成本。该文介绍了面向修复计算的目的及定义,并从认知心理学的角度讨论了人类出错的本质原因,最后从系统结构和操作系统的角度详细论述了面向修复计算的5个典型技术。     

Precise shape analysis using field sensitivity

We present a static shape analysis technique to infer the shapes of the heap structures created by a program at run time. Our technique is field sensitive in that it uses field information to compute the shapes. The shapes of the heap structures are computed using two components: (a) Boolean functions that capture the shape transitions due to the update of a field in a structure, and (b) through path matrices that store approximate path information between two pointer variables. We classify the shapes as one of Tree, Directed Acyclic Graph (DAG) and Cycle. The novelty of our approach lies in the way we use field information to remember the fields that cause a heap structure to have a particular shape (Tree, DAG or Cycle). This allows us to easily identify the field updates that cause shape transitions from Cycle to DAG, from Cycle to Tree and from DAG to Tree. This makes our analysis more precise as compared to earlier shape analyses that ignore the fields participating in the formation of a shape. We implemented our analysis in GCC as a dynamic plug-in as an interprocedural data-flow analysis and evaluated it on some standard benchmarks against a field-insensitive shape analysis technique as a baseline approach. We are able to achieve significant precision as compared to the baseline analysis (at the cost of increase in analysis time). In particular, we are able to infer more precise shapes for 4 out 7 Olden benchmarks, and never detect more cycles than the baseline analysis. We further suggest enhancements to improve the precision of our analysis under some constraints and to improve the analysis time at the cost of precision.     

Topological ordering algorithm for LDAG

Directed Acyclic Graph (DAG) is an important tool for workflow modeling and data provenance management. In these applications, DAG usually performs well. Yet for some workflow applications, except data or control dependencies between atomic tasks, there exists another requirement that each atomic task should be accomplished at an expected stage. Therefore, this paper proposes an improved DAG model – LDAG, in which each vertex has a level. Three cases of the level of vertices are discussed. For a reasonable one of these cases, this paper proposes a topological ordering algorithm and proves its correctness. In addition, it discusses the complexity of the algorithm and some other relevant problems.     

Sort-sharing-aware query processing

Many database applications require sorting a table (or relation) over multiple sort orders. Some examples include creation of multiple indices on a relation, generation of multiple reports from a table, evaluation of a complex query that involves multiple instances of a relation, and batch processing of a set of queries. In this paper, we study how to optimize multiple sortings of a table. We investigate the correlation between sort orders and exploit sort-sharing techniques of reusing the (partial) work done to sort a table on a particular order for another order. Specifically, we introduce a novel and powerful evaluation technique, called cooperative sorting, that enables sort sharing between seemingly non-related sort orders. Subsequently, given a specific set of sort orders, we determine the best combination of various sort-sharing techniques so as to minimize the total processing cost. We also develop techniques to make a traditional query optimizer extensible so that it will not miss the truly cheapest execution plan with the sort-sharing (post-) optimization turned on. We demonstrate the efficiency of our ideas with a prototype implementation in PostgreSQL and evaluate the performance using both TPC-DS benchmark and synthetic data. Our experimental results show significant performance improvement over the traditional evaluation scheme.     

A Robust Compile Time Method for Scheduling Task Parallelism on Distributed Memory Machines

The problem of compile time scheduling of tasks of a program represented as a directed acyclic graph (DAG) is NP-hard in its general form. A number of approaches have been proposed which attempt to solve the problem either sub-optimally for general cases or optimally for restrictive special cases. But all the compile time approaches suffer due to the inability to accurately model the computation and communication costs of the target architecture. A desirable property of a compile time scheduling algorithm is robustness against the variations in the computation and communication costs so that the run time performance is close to the compile time estimates; this aspect of scheduling has been left open in the literature.This paper first introduces a compile time scheduling algorithm for a variable number of available processors and then examines the impact of change of computation and communication costs on the generated schedule. The cost variations for all the nodes and all the edges are assumed to be uniform (in other words, all the node costs change by the same ratio and the edge costs change by the same ratio). This sort of variations could occur due to the inaccuracies in estimating the instruction execution times or the message passing delays. The ratio of the schedule length obtained by the new schedule based on the modified costs to the schedule length obtained by using the modified costs on the original schedule (obtained by initial costs) is used as a measure of the robustness of the algorithm. The essential conditions for robustness of the proposed algorithm are discussed and are demonstrated through an experimental study.     

双通道时延脉冲耦合神经网络的AOV-网拓扑排序

在时延脉冲耦合神经网络DPCNN的基础上提出了双通道时延脉冲耦合神经网络(DCDPCNN,Dual Channels DPCNN)模型,并提出了利用DCDPCNN来实现AOV-网拓扑排序算法。该算法在深度优先搜索的同时兼顾广度优先搜索,同时忽略节点进栈顺序,在求得的拓扑序列的个数、计算中的临时数据量、有向环判断、计算速度方面,比传统算法有了较大的改进。