英国研究生论文 Project Scheduling Construction Essay 屋面工程的生产率

管理建设项目总是需要不断的监测项目的性能和项目进度的更新。从施工现场的项目绩效数据和生产力数据是评估和预测项目绩效的关键作用，在长期的成本和进度。根据该等。（1995），在建设中的生产力一直是很难测量和控制的。即使在建设的生产力很难衡量，有必要使用它的及时决策，并减少对成本和进度的负面影响。因此，整合的历史生产力数据与建筑领域的持续的性能数据是必需的（Hwang和刘，2005）。

Hwang和刘，（2009）提出了一个准确的生产力预测是重要的管理建设项目。事实上，一个建筑项目通常是成功的，当它交付在其预算和时间表。计划和控制过程，也说明了准确估计这第一生产力的重要性，估计时间和建设活动，必然要求生产成本估算（欣泽，1998），第二，综合管理的时间和成本必须涉及的生产力因素（Dawood和莫尔森，1997）。

有大多数以前的研究侧重于确定因素影响生产力和测量有限的部分活动在微观层面上，探讨因素和生产力之间的关系。一个标准的施工生产率指标体系，更可靠的是施工生产率绩效评价和改进过程中的关键要素。有需要一个工具来驱动性能改进，通过内部和外部的基准。公园，等，（2005）目前，从建筑业的需求发展可接受的建设生产力指标和标准化的生产力数据是重点由行业参与者和研究的主要问题。

Abstract: In Malaysia, there is no research done specifically on collecting construction productivity data to serve as a guideline and standards for use by all players in the construction industry. Similar to JKR, as the largest implementers’ of government projects, the current practice on approval the schedule of works that submitted by contractors are mostly based on the engineers ‘gut’ feeling/opinion and past experience. There is no standard procedure and guidelines to review and checking the duration and resources allocation in each activity in contractor’s work program. As an effort, this study will start with focusing on the identification of factors that influencing roofing works productivity in construction project. Those factors can be obtained through a series of statistical analysis based on the data collected from questionnaire and interview with the experts. Data from previous projects also very important in order to get the actual productivity rate for roof works. Finally, the expected finding for this study will be: a) List of factors that influence the productivity rate for roof works, b) The critical factors affecting most to productivity rate for roofing works, c) A metrics of productivity rate for roof works as a standard and guideline for improving JKR projects scheduling.

1.0 INTRODUCTION

Managing construction projects always requires constant monitoring of project performance and the updating on project schedule. Project performance data and productivity data from the construction field is a key role in evaluating and predicting project performance in term of cost and schedule. According to Motwani et al. (1995), the productivity in construction has always been very difficult to measure and control. Even though, productivity in construction hard to measure, there is a need to use it for timely decisions and reduce the negative impacts on cost and schedule. Therefore, the integration of historical productivity data with the on-going performance data in construction field are required (Hwang and Liu, 2005).

Hwang and Liu, (2009) presented that an accurate productivity prediction is important for managing construction projects. In fact, a construction project is commonly to be successful when it is delivering within its budget and timeline. The planning and control process also explains the importance of accurate productivity estimate which first, estimating time and cost of construction activities that inevitably requires productivity estimates (Hinze, 1998), and second, integrated management of time and cost must involved productivity factor (Dawood and Molson, 1997).

There are most previous studies focusing on defining factors influence productivity and measuring limited parts of activities at micro level to investigate the relationship between factors and productivity. A standard construction productivity metric system that more reliable is a critical element in construction productivity performance evaluation and improvement process. There need a tool to drive performance improvement through internal and external benchmarking. Park, et al., (2005) present that nowadays, demand from the construction industry on developing acceptable construction productivity metrics and standardized productivity data are the main issues to focus by the industry players and researches.