Identification of Repetitive Processes at Steady- and Unsteady-state: Transfer Function

TL;DR

This paper explores the use of transfer functions to model and identify the dynamic behavior of repetitive processes in construction projects under various state conditions, aiming to improve planning and control.

Contribution

It develops an explicit transfer function-based model that captures the dynamics of repetitive processes in transient, steady, and unsteady states in construction.

Findings

Transfer function effectively models process behavior across different states.

The model accurately reproduces the process dynamics from offshore oil well construction data.

Provides a mathematical basis for improved project planning and control.

Abstract

Projects are finite terminating endeavors with distinctive outcomes, usually, occurring under transient conditions. Nevertheless, most estimation, planning, and scheduling approaches overlook the dynamics of project-based systems in construction. These approaches underestimate the influence of process repetitiveness, the variation of learning curves and the conservation of processes' properties. So far, estimation and modeling approaches have enabled a comprehensive understanding of repetitive processes in projects at steady-state. However, there has been little research to understand and develop an integrated and explicit representation of the dynamics of these processes in either transient, steady or unsteady conditions. This study evaluates the transfer function in its capability of simultaneously identifying and representing the production behavior of repetitive processes in different state conditions. The sample data for this research comes from the construction of an offshore oil well and describes the performance of a particular process by considering the inputs necessary to produce the outputs. The result is a concise mathematical model that satisfactorily reproduces the process' behavior. Identifying suitable modeling methods, which accurately represent the dynamic conditions of production in repetitive processes, may provide more robust means to plan and control construction projects based on a mathematically driven production theory.