UCC项目总承包工程建设的过程管理

由成都建筑材料工业设计研究院有限公司(简称成都院)和中国建材装备有限公司组成联合体总承包的UCC 10 000 t/d熟料水泥生产线,成都院负责提供合同范围内所有的工程设计、核心技术及核心设备、现场管理、人员培训、生产线达标考核,并具体负责实施和完成所有的土建及安装工程。在总承包工程建设的过程管理中,成都院以设计为灵魂,贯彻了"工艺流程顺畅,总图布局合理,系统配置先进可靠,合理节省投资"的设计理念;成都院克服了要求高、工期短、气候条件恶劣、国外施工人员管理难度大、功效低等重重困难,按时、保质保量、安全地完成了土建、安装、调试和试车任务;2007年4月26日,该生产线一次性通过业主的达标考核。我们的经验是:(1)有专业设计院全面介入的工程总承包是项目顺利进行的可靠保证;(2)参与建设的相关单位要相互信任、相互配合、目标一致;(3)工程建设管理科学化、人性化很重要。该项目的成功建设,使成都院总包工程建设的过程管理更加正规化、合理化和科学化。     

Fuzzy arithmetic based reliability allocation approach during early design and development

During early design and development stages, every engineering system has to meet its specific reliability goals. The target reliability of the system is achieved through application of an effective reliability apportionment technique to its subsystems. There are various traditional methods exist to perform the reliability allocation based on engineering factors that are assessed in a subjective manner. The conventional reliability allocation approach requires the assessment of factors like complexity, cost, and maintenance. This may not be realistic in real applications if they are assessed in a crisp manner by the domain experts of their varied expertise and background.In this paper, we treat allocation factors as fuzzy numbers, which are evaluated in fuzzy linguistic terms. As a result, fuzzy proportionality factor scales are proposed for the subsystems. In order to accomplish fuzzy division to evaluate the fuzzy proportionality factor, an approximation method based on linear programming for trapezoidal fuzzy numbers is also proposed in this paper. For the evaluation of weighting factors from fuzzy proportionality factors, centroid method of defuzzification is being employed. The allocated reliability of each subsystem is computed with the help of weighting factor thereafter. An example is provided to illustrate the potential application of the proposed fuzzy based reliability allocation approach.     

A hybrid vendor managed inventory and redundancy allocation optimization problem in supply chain management: An NSGA-II with tuned parameters

In this research, a bi-objective vendor managed inventory model in a supply chain with one vendor (producer) and several retailers is developed, in which determination of the optimal numbers of different machines that work in series to produce a single item is considered. While the demand rates of the retailers are deterministic and known, the constraints are the total budget, required storage space, vendor's total replenishment frequencies, and average inventory. In addition to production and holding costs of the vendor along with the ordering and holding costs of the retailers, the transportation cost of delivering the item to the retailers is also considered in the total chain cost. The aim is to find the order size, the replenishment frequency of the retailers, the optimal traveling tour from the vendor to retailers, and the number of machines so as the total chain cost is minimized while the system reliability of producing the item is maximized. Since the developed model of the problem is NP-hard, the multi-objective meta-heuristic optimization algorithm of non-dominated sorting genetic algorithm-II (NSGA-II) is proposed to solve the problem. Besides, since no benchmark is available in the literature to verify and validate the results obtained, a non-dominated ranking genetic algorithm (NRGA) is suggested to solve the problem as well. The parameters of both algorithms are first calibrated using the Taguchi approach. Then, the performances of the two algorithms are compared in terms of some multi-objective performance measures. Moreover, a local searcher, named simulated annealing (SA), is used to improve NSGA-II. For further validation, the Pareto fronts are compared to lower and upper bounds obtained using a genetic algorithm employed to solve two single-objective problems separately.     

User experience and personal innovativeness: An empirical study on the Enterprise Resource Planning systems

Although user experience and personal innovativeness are two important factors in new technology adoption, there has been no prior study to test these factors with the Enterprise Resource Planning (ERP) adoption. This paper investigates moderating roles of user experience on the relationship between the personal innovativeness and the ERP adoption motivations. This issue is important because if the user has more experience with the systems then the power of influence of personal innovativeness on ERP adoption motivation would be different. Thus, this paper tests these important insights of ERP systems adoption with the two different field samples with high (more than three years) and low (less than three years) user experience, based on the innovation diffusion theory, self determination theory, and different types of motivations such as intrinsic and extrinsic motivations. The findings, based on the PLS analysis of the model using 107 ERP end users, show that there are clear moderating effects of user experience—such as impacts of personal innovativeness on ERP systems adoption motivations are higher in case of low user experience samples, as expected. Academic and practical implications are discussed in the paper based on these empirical findings.     

许昌市水资源多模式联合调度与合理配置

水资源短缺是制约城市经济社会发展的重要因素,合理配置和有效利用有限的水资源是解决水资源供需矛盾的有效途径。以许昌市国家水生态文明试点城市建设为例,在分析水资源开发利用现状的基础上,运用定额法对需水量进行预测。以多年平均水资源量为基础,2011年作为研究基准年,根据水资源取用方式、工程方案及各种水量需求等,利用MIKE BASIN建立水资源配置模型,对许昌市水资源供需平衡进行模拟,通过三次供需平衡分析,得到不同规划水平年水资源供需配置方案,最终达到水资源供需平衡,使有限的水资源发挥最大效益。研究结果表明,本地水资源结合南水北调、引黄调蓄、中水回用等多模式联合调度,可以满足许昌市中长期经济社会可持续发展用水需求,对未来许昌市水资源可持续利用具有重要的指导意义。     

基于非负矩阵分解的水量分配方案后评价

提出了水量分配方案后评价指标体系,建立了基于非负矩阵分解的水量分配方案后评价模型,并以1983年国务院批复的引滦水量分配方案为研究对象进行了水量分配方案诊断式后评价。该方法利用非负矩阵分解算法将指标矩阵最大程度分解为基向量和权向量,根据权向量元素的数值对水量分配方案进行评级,根据基向量元素的数值判断评价指标的实施状况。通过对引滦水量分配方案质量、实施过程、实施效果和可持续性的调查分析,得出方案综合后评价结果为优,但在来用水可持续性、对水环境影响及水质目标保证率等方面需加以改进。评价结果表明,基于非负矩阵分解原理的后评价方法评价效果较好,可为水量分配方案等项目的后评价提供科学依据。     

Cloud based intelligent system for delivering health care as a service

The promising potential of cloud computing and its convergence with technologies such as mobile computing, wireless networks, sensor technologies allows for creation and delivery of newer type of cloud services. In this paper, we advocate the use of cloud computing for the creation and management of cloud based health care services. As a representative case study, we design a Cloud Based Intelligent Health Care Service (CBIHCS) that performs real time monitoring of user health data for diagnosis of chronic illness such as diabetes. Advance body sensor components are utilized to gather user specific health data and store in cloud based storage repositories for subsequent analysis and classification. In addition, infrastructure level mechanisms are proposed to provide dynamic resource elasticity for CBIHCS. Experimental results demonstrate that classification accuracy of 92.59% is achieved with our prototype system and the predicted patterns of CPU usage offer better opportunities for adaptive resource elasticity.     

Modeling and global trajectory tracking control for an over-actuated MAV

This paper deals with an original micro aerial vehicle (MAV) design, the Omnicopter MAV. It has two central coaxial rotors with fixed-pitch propellers and three perimeter mounted ducted fans with servo motors performing thrust vectoring. Compared with traditional rotary wing MAVs that have inherent underactuation, the Omnicopter possesses some advantages in mobility, for example, lateral translation with zero attitude and hover with nonzero attitude. The trajectory tracking control design, global stability analysis, and control allocation are demonstrated through numerical simulation. The advantage of zero attitude translation is illustrated through experimental results.     

Rescheduling policies for large-scale task allocation of autonomous straddle carriers under uncertainty at automated container terminals

This paper investigates replanning strategies for container-transportation task allocation of autonomous Straddle Carriers (SC) at automated container terminals. The strategies address the problem of large-scale scheduling in the context of uncertainty (especially uncertainty associated with unexpected events such as the arrival of a new task). Two rescheduling policies–Rescheduling New arrival Jobs (RNJ) policy and Rescheduling Combination of new and unexecuted Jobs (RCJ) policy–are presented and compared for long-term Autonomous SC Scheduling (ASCS) under the uncertainty of new job arrival. The long-term performance of the two rescheduling policies is evaluated using a multi-objective cost function (i.e., the sum of the costs of SC travelling, SC waiting, and delay of finishing high-priority jobs). This evaluation is conducted based on two different ASCS solving algorithms–an exact algorithm (i.e., branch-and-bound with column generation (BBCG) algorithm) and an approximate algorithm (i.e., auction algorithm)–to get the schedule of each short-term planning for the policy. Based on the map of an actual fully-automated container terminal, simulation and comparative results demonstrate the quality advantage of the RCJ policy compared with the RNJ policy for task allocation of autonomous straddle carriers under uncertainty. Long-term testing results also show that although the auction algorithm is much more efficient than the BBCG algorithm for practical applications, it is not effective enough, even when employed by the superior RCJ policy, to achieve high-quality scheduling of autonomous SCs at the container terminals.     

Hybrid shuffled frog leaping algorithm for energy-efficient dynamic consolidation of virtual machines in cloud data centers

Cloud computing aims to provide dynamic leasing of server capabilities as scalable virtualized services to end users. However, data centers hosting cloud applications consume vast amounts of electrical energy, thereby contributing to high operational costs and carbon footprints. Green cloud computing solutions that can not only minimize the operational costs but also reduce the environmental impact are necessary. This study focuses on the Infrastructure as a Service model, where custom virtual machines (VMs) are launched in appropriate servers available in a data center. A complete data center resource management scheme is presented in this paper. The scheme can not only ensure user quality of service (through service level agreements) but can also achieve maximum energy saving and green computing goals. Considering that the data center host is usually tens of thousands in size and that using an exact algorithm to solve the resource allocation problem is difficult, the modified shuffled frog leaping algorithm and improved extremal optimization are employed in this study to solve the dynamic allocation problem of VMs. Experimental results demonstrate that the proposed resource management scheme exhibits excellent performance in green cloud computing.