Estimating Optimum Staffing Levels via Predicting Workload and Resources Requirements for Engineering and Construction Firms

通过预测工程和建筑公司的工作量和资源需求来估计最佳人员配置水平

• 批准号: RGPIN-2020-03955
• 负责人: Hammad, Ahmed
• 金额: $ 1.68万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=716735
• 关键词: Estimating; Optimum; Staffing; Levels; via

Nearly all Engineering and Construction firms rely on continuous supply of projects to generate their revenue and stay in business. These companies plan and execute multiple projects simultaneously utilizing one common pool of resources. In order to estimate staffing levels efficiently, firms have to predict expected workload and amounts of resources required to perform this expected workload. Both these previously mentioned tasks have high levels of uncertainty. Although, there is a large amount of academic studies that addressed the problem of resource allocation, leveling and optimization, most of theses studies focused on studying projects independently from each other, not on managing multiple projects using one common pool of resources. Hence, the provided solutions cannot be applied to real life complex situations. The long-term goal of this research program is to develop an integrated framework to estimate optimum staffing levels via predicting workload and resources requirements for Engineering and Construction firms. The research will be performed by four PhD students as follows: First student will investigate various options of smart data acquisition of resources utilization data at the work package level. The student will focus on utilization of parametric features included in Building Information Modeling (BIM) to store the data. Second student will work on predicting future workload using System Dynamics (SD). The approach will focus on capturing the impact of internal and external factors affecting the probability of wining new projects by an organization. Since recourse utilization data is time-dependent, the third student will implement Sequential Pattern Analysis (SPA) data analytics technique to predict resource requirements for upcoming projects. Fourth student will develop Knowledge-Based Decision Support Systems (KBDSS) bases on multiple-objectives optimization techniques to estimate future resource levels. This research program will introduce new algorithms and techniques that have not been widely used in construction academic research. The quantitative approach of this research will investigate innovative methods to combine various algorithms in one integrated framework to achieve optimum solutions. The program will bring new insights by longitudinally exploring data relating to construction and comprehensively considering data collected from multiple projects. The research program is also expected to benefit the industry by introducing a practical solution that can be implemented to address one of their major challenges. With the vast amount of hours spent in construction projects and, multi-billion dollar investments, improvements by this research will have significant benefit to industry and society.

几乎所有的工程和建筑公司都依赖于项目的持续供应来产生收入并保持业务。这些公司利用一个共同的资源池同时计划和执行多个项目。为了有效地估计人员配备水平，公司必须预测预期的工作量和执行预期工作量所需的资源量。前面提到的这两项任务都具有很高的不确定性。 虽然，有大量的学术研究，解决了资源分配，均衡和优化的问题，大多数这些研究集中在研究项目相互独立，而不是使用一个共同的资源池管理多个项目。因此，所提供的解决方案不能应用于真实的生活复杂情况。 该研究计划的长期目标是开发一个综合框架，通过预测工程和建筑公司的工作量和资源需求来估计最佳人员配置水平。该研究将由四名博士生进行，具体如下： 首先，学生将研究在工作包级别的资源利用数据的智能数据采集的各种选项。学生将专注于利用建筑信息建模（BIM）中包含的参数化特征来存储数据。 第二个学生将使用系统动力学（SD）预测未来的工作量。该方法将侧重于捕捉影响一个组织赢得新项目的可能性的内部和外部因素的影响。 由于资源利用数据是时间依赖的，第三个学生将实施序列模式分析（SPA）数据分析技术，以预测即将到来的项目的资源需求。 第四，学生将开发基于知识的决策支持系统（KBDSS）基于多目标优化技术，以估计未来的资源水平。 该研究计划将介绍在建筑学术研究中尚未广泛使用的新算法和技术。本研究的定量方法将探讨创新的方法，将联合收割机的各种算法在一个综合框架，以实现最佳的解决方案。该计划将通过纵向探索与建筑相关的数据并综合考虑从多个项目收集的数据来带来新的见解。该研究计划还有望通过引入一种实用的解决方案来解决其主要挑战之一，从而使该行业受益。由于在建筑项目上花费了大量的时间和数十亿美元的投资，这项研究的改进将对工业和社会产生重大利益。