Hierarchical multi-reservoir optimization modeling for real-world complexity with application to the Three Gorges system

High dimensionality in real-world multi-reservoir systems greatly hinders the application and popularity of evolutionary algorithms, especially for systems with heterogeneous units. An efficient hierarchical optimization framework is presented for search space reduction, determining the best water distributions, not only between cascade reservoirs, but also among different types of hydropower units. The framework is applied to the Three Gorges Project (TGP) system and the results demonstrate that the difficulties of multi-reservoir optimization caused by high dimensionality can be effectively solved by the proposed hierarchical method. For the day studied, power output could be increased by 6.79 GWh using an optimal decision with the same amount of water actually used; while the same amount of power could be generated with 2.59 × 10⁷ m³ less water compared to the historical policy. The methodology proposed is general in that it can be used for other reservoir systems and other types of heterogeneous unit generators.     

BIM技术应用于建筑工程造价的对策研究

随着BIM技术的进步,建筑行业越来越重视BIM技术的应用,并在应用过程中取得一定的成效。本文从工程造价决策、设计、招投标、施工、竣工五个阶段阐述BIM技术在建筑工程造价中的应用对策。     

CMP系统技术与市场

概述了CMP系统技术的发展历史、发展趋势以及在IC生产中的重要性,介绍了国外CMP设备主要制造厂家的设备型号和性能及CMP设备市场分布和需求,阐述了CMP系统技术的基础研究、关键技术和国内研究概况。     

船闸直立墙基坑开挖与锚固施工程序

三峡船闸深槽开挖与锚固工程量大，工期紧，施工强度高，施工难度大。因此，合理的施工程序有利于边坡稳定和施工，是快速和高质量施工的关键。分析了永久船闸直立墙基坑开挖与锚固施工程序的复杂性和进行研究的必要性。介绍了设计阶段研究船闸开挖与锚固的施工程序。对施工过程中研究施工程序时考虑的因素，研究的主要内容以及实际采用的施工程序作了简要的分析，归纳和总结。     

军事科研项目中软件项目管理方法研究

列举分析了目前软件开发中存在的问题，提出了在军事科研项目中，对软件项目实施管理的方法，应用先进的软件项目管理理论和方法，有效的提高软件开发效率，确保软件质量。     

黄河黑山峡河段大柳树水库比小观音水库原始库容多40亿m3的作用

一级开发大柳树高坝原始库容比二级开发小观音高坝多 4 0亿m3 ,库容大带来的好处体现在 5～ 7月可多向下游增供水量 ,缓解下游河道断流 ;延长泄放清水时间 ;增加发电效益 ;满足宁蒙河段防凌要求 ;增加汛期集中泄水 ,减缓下游河道淤积萎缩 ;满足南水北调西线工程水量调节要求等。为了更好优化配置黄河水资源 ,从全局和长远利益考虑 ,应采用一级开发方案 ,修建大柳树高坝大库     

重视过程控制工程安装材料的选择

本文结合工程介绍了自动化仪表工程设计中常用安装材料的正确选择方法和选择不当可能造成的危害。     

Improving Cost Estimates of Construction Projects Using Phased Cost Factors

Central to cost-based competition is the capability to accurately predict the cost of delivering a project. Most literature on cost estimation focuses on specific estimation methods as generic techniques and little attention has been paid to the unique requirements at each project stage. This note attempts to identify the critical factors for effective estimation at various stages of typical construction projects. Drawing from organization control theory and cost estimating literature, this note develops a theoretical framework that identifies the critical factors for effective cost estimation during each project phase of a conventional construction project. The underlying logic is that as a cost estimating effort progresses, both task programmability and output measurability improve. As a result, control effort will shift from input-oriented control to a combination of output and behavior control.     

Developing Cost Response Models for Company-Level Cost Flow Forecasting of Project-Based Corporations

The difficulty in applying the standard curve (S-curve) and cost-schedule integration (CSI) techniques for company-level cost flow forecasting in a project-based industry is the prerequisite of forecasting future unknown individual projects and contract classifications. By analyzing cost flows at the company level through a pool of macroeconomic and internal financial data, this paper proposes an innovative approach to firm-specific model estimation. First, a series of data transformations introduce linear relationships between cost, macroeconomic, and internal financial variables. Second, multivariate regression analysis is employed for initial model building. Third, for the purposes of model restructuring, a subsequent application of Yule–Walker estimates and incomplete principal component analysis is used. This paper uses a sample of four project-based construction firms to demonstrate model performance. Using this methodology, mean absolute percentage error (MAPE) values of the forecasting models range from 0.27 to 0.60%. As such, the transformed cost, macroeconomic, internal financial data could strongly predict company-level cost flow forecasting. While converting the predicted cumulative cost data to periodic cost flows, the MAPE values were augmented, ranging from 7.04 to 17.55%, thus, requiring future research.