浅谈计算机管理信息系统的发展及其经济效益

在社会各行各业竞争日趋激烈的形势下,有效、科学的管理是企业生存的必备条件.其中以信息管理为重要手段,数据的收集处理的快速准确化,为企业降低成本,带来更好的经济收益,也使得企业在生产过程中对组织内部进行更合理有效的调整,对组织外部的各类企业有更好的交流,而本身的重大决策,也有了更加科学的依据.本文介绍了信息系统的发展,以及如何带来经济收益.     

首钢数据分析与决策支持系统在财务成本管理中的应用

通过管理模型的应用,实现了利用财务成本倒推法来控制生产成本。通过逆向成本管理实现对财务成本的有效控制,并收到良好效果。     

一种基于视觉特征的形状渐变算法

基于建立两个平面形状特征点之间的对应关系的问题上,提出一种新的解决方法。这种方法是把特征点从源形状和目标形状中抽取出来,根据此方法来衡量两个特征点之间相似度,采用有效的动态编程的方法找出特征点之间的最优化的对应关系来解决问题。该算法简洁、快速,并有效地保留原始形体上的特征属性。     

Optimal Trajectory Planning for Wheeled Mobile Robots Based on Kinematics Singularity

This research introduces a new optimality criterion for motion planning of wheeled mobile robots based on a cost index that assesses the nearness to singularity of forward and inverse kinematic models. Slip motions, infinite estimation error and impossible control actions are avoided escaping from singularities. In addition, high amplification of wheel velocity errors and high wheel velocity values are also avoided by moving far from the singularity. The proposed cost index can be used directly to complement path-planning and motion-planning techniques (e.g. tree graphs, roadmaps, etc.) in order to select the optimal collision-free path or trajectory among several possible solutions. To illustrate the applications of the proposed approach, an industrial forklift, equivalent to a tricycle-like mobile robot, is considered in a simulated environment. In particular, several results are validated for the proposed optimality criterion, which are extensively compared to those obtained with other classical optimality criteria, such as shortest-path, time-optimal and minimum-energy.     

An ant colony optimization approach to multi-objective optimal design of symmetric hybrid laminates for maximum fundamental frequency and minimum cost

An ant colony optimization algorithm for optimum design of symmetric hybrid laminates is described. The objective is simultaneous maximization of fundamental frequency and minimization of cost. Number of surface and core layers made of high-stiffness and low-stiffness materials, respectively, and fiber orientations are the design variables. Optimal stacking sequences are given for hybrid graphite/epoxy-glass/epoxy laminated plates with different aspect ratios and number of plies. The results obtained by ant colony optimization are compared to results obtained by a genetic algorithm and simulated annealing. The effectiveness of the hybridization concept for reducing the weight and keeping the fundamental frequency at a reasonable level is demonstrated. Furthermore, it is shown that the proposed ant colony algorithm outperforms the two other heuristics.     

The cost of approximate controllability for semilinear heat equations

We consider the semilinear heat equation with globally Lipschitz non-linearity involving gradient terms in a bounded domain of R^n. In this paper, we obtain explicit bounds of the cost of approximate controllability, i.e., of the minimal norm of a control needed to control the system approximately. The methods we used combine global Carleman estimates, the variational approach to approximate controllability and Schauder＇s fixed point theorem.     

Cost Effective Optimal Task Scheduling Model in Hybrid Cloud Environment

In today’s world, Cloud Computing (CC) enables the users to access computing resources and services over cloud without any need to own the infrastructure. Cloud Computing is a concept in which a network of devices, located in remote locations, is integrated to perform operations like data collection, processing, data profiling and data storage. In this context, resource allocation and task scheduling are important processes which must be managed based on the requirements of a user. In order to allocate the resources effectively, hybrid cloud is employed since it is a capable solution to process large-scale consumer applications in a pay-by-use manner. Hence, the model is to be designed as a profit-driven framework to reduce cost and make span. With this motivation, the current research work develops a Cost-Effective Optimal Task Scheduling Model (CEOTS). A novel algorithm called Target-based Cost Derivation (TCD) model is used in the proposed work for hybrid clouds. Moreover, the algorithm works on the basis of multi-intentional task completion process with optimal resource allocation. The model was successfully simulated to validate its effectiveness based on factors such as processing time, make span and efficient utilization of virtual machines. The results infer that the proposed model outperformed the existing works and can be relied in future for real-time applications.     

Integrated Risk of Progress-Based Costs and Schedule Delays in Construction Projects

This article aims to explore the impact of the integration of risk factors into delayed milestones for construction projects. A simulation model was developed to determine the impact of schedule variability on cost estimation. To generate random scenarios a Monte Carlo Simulation (MCS) technique was applied. The developed model computes the cost impact of delayed milestone in the expected budget. Using a risk integration approach revealed the critical time frame that may lead to a budget deficit for a project. As a result, a number of cost-sensitive risk factors and schedule delays were identified for the critical time period where the risk of budget deficit increases. The method of integration proposed in this article highlights the priority of risk factors and schedule delays for construction contracts involving Payments at Event Occurrences (PEO). Consequently, the developed method can be useful for practitioners in anticipation of potential increase of costs, hence, prevention of failure due to budget deficit.     

On-demand minimum cost benchmarking for intermediate dataset storage in scientific cloud workflow systems

Many scientific workflows are data intensive: large volumes of intermediate datasets are generated during their execution. Some valuable intermediate datasets need to be stored for sharing or reuse. Traditionally, they are selectively stored according to the system storage capacity, determined manually. As doing science on clouds has become popular nowadays, more intermediate datasets in scientific cloud workflows can be stored by different storage strategies based on a pay-as-you-go model. In this paper, we build an intermediate data dependency graph (IDG) from the data provenances in scientific workflows. With the IDG, deleted intermediate datasets can be regenerated, and as such we develop a novel algorithm that can find a minimum cost storage strategy for the intermediate datasets in scientific cloud workflow systems. The strategy achieves the best trade-off of computation cost and storage cost by automatically storing the most appropriate intermediate datasets in the cloud storage. This strategy can be utilised on demand as a minimum cost benchmark for all other intermediate dataset storage strategies in the cloud. We utilise Amazon clouds’ cost model and apply the algorithm to general random as well as specific astrophysics pulsar searching scientific workflows for evaluation. The results show that benchmarking effectively demonstrates the cost effectiveness over other representative storage strategies.     

Deadline division-based heuristic for cost optimization in workflow scheduling

Cost optimization for workflow applications described by Directed Acyclic Graph (DAG) with deadline constraints is a fundamental and intractable problem on Grids. In this paper, an effective and efficient heuristic called DET (Deadline Early Tree) is proposed. An early feasible schedule for a workflow application is defined as an Early Tree. According to the Early Tree, all tasks are grouped and the Critical Path is given. For critical activities, the optimal cost solution under the deadline constraint can be obtained by a dynamic programming strategy, and the whole deadline is segmented into time windows according to the slack time float. For non-critical activities, an iterative procedure is proposed to maximize time windows while maintaining the precedence constraints among activities. In terms of the time window allocations, a local optimization method is developed to minimize execution costs. The two local cost optimization methods can lead to a global near-optimal solution. Experimental results show that DET outperforms two other recent leveling algorithms. Moreover, the deadline division strategy adopted by DET can be applied to all feasible deadlines.