Publications / 2019 Proceedings of the 36th ISARC, Banff, Alberta, Canada

Schedule Uncertainty Analysis System Framework to Manage and Allocate Historical Data for Industrial Construction Project

In Seok Yoon, Hyun-Soo Lee, Moonseo Park, Jin Gang Lee and Sang Sun Jung
Pages 1184-1188 (2019 Proceedings of the 36th ISARC, Banff, Alberta, Canada)

Many project managers and researchers have emphasized the importance of considering uncertainty in the scheduling of EPC projects, and perform stochastic analysis when preparing a schedule to reflect uncertainties of duration for project activities. In this case, the uncertainty of the individual activity duration is treated as probabilistic variables, and this probability should be estimated based on previous project experience and data. To use past project data for probabilistic schedule analysis of planned schedules, the schedule information of past project should be stored and managed systematically, and the analysis should be based on the relevant past project data. Existing schedule analysis tools are focused on the analysis process, and there is a lack of consideration on how to manage and apply the past project data needed for the schedule analysis. Therefore, this paper proposes a system framework that enables probabilistic schedule analysis, which automatically supports the process of identifying and allocating corresponding past project activities for estimating activity durations of a planned schedule. We propose a data model and system framework that reflects defined issues and the requirements. The framework is composed of 4 modules: As-built schedule data storage module, a planned schedule input module, a historical data allocation module, and uncertainty analysis module. It is expected that the proposed system framework will contribute to overcoming the difficulties of data management for collection and planned schedule analysis in practice.

Keywords: Schedule Analysis; Probabilistic Analysis; Uncertainty; System Framework; Data Model