Optimization of change order management process with object-oriented discrete event simulation: Case study

Du, J; El-Gafy, M and Zhao, D (2016) Optimization of change order management process with object-oriented discrete event simulation: Case study. Journal of Construction Engineering and Management, 142(4), ISSN 0733-9364

Abstract

Change orders are common to most construction projects. They can significantly increase project cost and duration, leading to more claims and disputes and ultimately creating an adversarial relationship among project members. Evidence has shown that a contributing factor to the inefficiency of change order management is the management process utilized in most construction projects, which always relates to suboptimal allocation of resources and unnecessary procedures. Discrete event simulation (DES) provides an effective approach to streamline the change order management process by evaluating a series of improvement options. Based on a comparison of two prevailing DES paradigms, activity scanning (AS) and process interaction (PI), this paper presents an object-oriented DES model to investigate the change order management process. A case study has been performed to investigate the change order management process at a Midwestern land-grant university with the proposed simulation model, where the bottlenecks of as-is process have been identified and improved. The developed model employs PI paradigm rather than AS paradigm because the former is capable of capturing the real time state changes of change orders. Sensitivity analysis (SA) is also applied to examine the quantitative impacts of changeable variables to evaluate improvement options. The results indicate that PI paradigm outperforms AS in the investigation of change order management process. It is also expected that the developed model provides an optimization tool to support change order management.

Item Type: Article
Uncontrolled Keywords: activity scanning; change order; discrete event simulation; object-oriented programming; process interaction; quantitative methods; sensitivity analysis
Date Deposited: 11 Apr 2025 19:46
Last Modified: 11 Apr 2025 19:46