Application of Fuzzy Logic to Simulation for Construction Operations

This paper describes the application of fuzzy logic to discrete event simulation in dealing with uncertainties of construction operations. The uncertainties in the quantity of resources required to activate an activity are modeled with fuzzy sets in linguistic terms. The fuzzy logic if-then rule is built to control the activation of activities. The duration of the activity that varies with the quantities of resources involved is determined through the fuzzy logic rule-based model. The fuzzy logic control of activities is incorporated with the activity scanning simulation strategy to implement the fuzzy simulation system for construction operations. In addition, the fuzzy activity element is adopted in the graphical modeling process. Examples are given that illustrate uses of the fuzzy simulation system and the impact of flexible demand of resources on productivity.

[1]  R. Deal Simulation Modeling and Analysis (2nd Ed.) , 1994 .

[2]  H. Ishii,et al.  Single machine scheduling problem with fuzzy precedence relation , 1995 .

[3]  Lotfi A. Zadeh,et al.  Fuzzy Logic Control for Business, Finance, and Management , 1997 .

[4]  Michael Pidd,et al.  Computer Simulation in Management Science (3rd Edition) , 1998 .

[5]  Jonathan Jingsheng Shi,et al.  ACTIVITY-BASED CONSTRUCTION (ABC) MODELING AND SIMULATION METHOD , 1999 .

[6]  Photios G. Ioannou,et al.  General-purpose systems for effective construction simulation , 1999 .

[7]  K. Ronald Laughery,et al.  Advanced uses for Micro Saint simulation software , 1996, Winter Simulation Conference.

[8]  Lotfi A. Zadeh,et al.  Outline of a New Approach to the Analysis of Complex Systems and Decision Processes , 1973, IEEE Trans. Syst. Man Cybern..

[9]  Sofjan H. Nasution Techniques and Applications of Fuzzy Theory to Critical Path Methods , 1999 .

[10]  Iris D. Tommelein,et al.  Pull-Driven Scheduling for Pipe-Spool Installation: Simulation of Lean Construction Technique , 1998 .

[11]  Gregory A. Howell,et al.  Interaction between subcycles: one key to improved methods , 1993 .

[12]  Hamad I. Odhabi Java Iconic Visual Environment for Simulation (JIVESim) , 1999 .

[13]  Radivoj Petrovic,et al.  Modelling and simulation of a supply chain in an uncertain environment , 1998, Eur. J. Oper. Res..

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

[15]  Zhang Hong,et al.  Visual modeling and simulation for construction operations , 2002 .

[16]  Daniel W. Halpin,et al.  Planning and analysis of construction operations , 1992 .

[17]  Stephen E. Chick Steps to implement Bayesian input distribution selection , 1999, WSC '99.

[18]  Photios G. Ioannou,et al.  Graphical object-oriented discrete-event simulation system , 1992, WSC '92.

[19]  Tzung-Pei Hong,et al.  LPT scheduling for fuzzy tasks , 1998, Fuzzy Sets Syst..

[20]  James W Hopper Strategy-related characteristics of discrete-event languages and models , 1986 .

[21]  Iris D. Tommelein,et al.  Knowledge-based assembly of simulation networks using construction designs, plans, and methods , 1994, Proceedings of Winter Simulation Conference.

[22]  Carl G. Looney Fuzzy Petri Nets and Applications , 1994 .

[23]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Hum. Comput. Stud..

[24]  Photios G. Ioannou,et al.  General purpose simulation with Stroboscope , 1994, Proceedings of Winter Simulation Conference.