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

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.

[1]  Tariq S. Abdelhamid,et al.  The Self-Destruction and Renewal of Lean Construction Theory: A Prediction From Boyd's Theory , 2004 .

[2]  Howard H. Bashford,et al.  Production Equations for Unsteady-State Construction Processes , 2007 .

[3]  Onur Behzat Tokdemir,et al.  Effect of learning on line-of-balance scheduling , 2001 .

[4]  J. Berger.  Statistical Decision Theory and Bayesian Analysis , 1988 .

[5]  Lennart Ljung,et al.  System identification toolbox for use with MATLAB , 1988 .

[6]  Keith R. Molenaar,et al.  Multiobjective design of Work-In-Process buffer for scheduling repetitive building projects , 2009 .

[7]  James D. Lutz,et al.  Planning repetitive construction: Current practice , 1993 .

[8]  S. Billings.  Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains , 2013 .

[9]  Wallace J. Hopp,et al.  Factory physics : foundations of manufacturing management , 1996 .

[10]  Mani Poshdar,et al.  Characterization of Process Variability in Construction , 2014 .

[11]  J D Littler,et al.  A PROOF OF THE QUEUING FORMULA , 1961 .

[12]  Sven Bertelsen,et al.  Construction as a Complex System , 2003 .

[13]  Russell L. Purvis,et al.  Project Management under Uncertainty: The Impact of Heuristics and Biases , 2002 .

[14]  Leonhard E. Bernold.  Simulation of Nonsteady Construction Processes , 1989 .

[15]  Liuping Wang,et al.  Identification of Continuous-time Models from Sampled Data , 2008 .

[16]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[17]  Fred L. Collopy,et al.  Error Measures for Generalizing About Forecasting Methods: Empirical Comparisons , 1992 .

[18]  Katsuhiko Ogata,et al.  Modern Control Engineering , 1970 .

[19]  Sven Bertelsen,et al.  COMPLEXITY – CONSTRUCTION IN A NEW PERSPECTIVE , 2003 .

[20]  Alexander Laufer.  Simultaneous Management: Managing Projects in a Dynamic Environment , 1996 .

[21]  Mario C. Cirillo,et al.  On the use of the normalized mean square error in evaluating dispersion model performance , 1993 .

[22]  Vicente González,et al.  A Production Model for Construction: A Theoretical Framework , 2015 .

[23]  Wolfgang Altmann,et al.  Practical Process Control for Engineers and Technicians , 2005 .

[24]  Ravindra B. Bapat,et al.  Linear algebra and linear models , 2000 .

[25]  J. Little.  A Proof for the Queuing Formula: L = λW , 1961 .

[26]  Anthony Mills,et al.  A systematic approach to risk management for construction , 2001 .