A decision-making system for construction site layout planning

Abstract A decision-making system, which consists of input, design, evaluation and selection, and output stages, is proposed to solve dynamic, multi-objective and unequal-area construction site layout planning (CSLP) problem. In the input stage, the multiple objectives, schedule planning and site condition are determined. In the design stage, two mathematical optimization models max–min ant system (MMAS) and modified Pareto-based ant colony optimization (ACO) algorithm are employed to solve single objective optimization (SOO) and multi-objective optimization (MOO) problem respectively. In the evaluation and selection stage, the intuitionistic fuzzy TOPSIS method is used to evaluate and select the best layout plan among the generated layout alternatives from the design stage. The performance of the proposed decision-making system, which was verified by a residential building project, shall assist the practitioners in the construction industry to deliver construction projects in a more efficient and effective manner, and thus construction costs could be reduced significantly.

[1]  Iris D. Tommelein,et al.  Sightplan: an expert system that models and augments human decision-making for designing construction site layouts , 1989 .

[2]  Tarek Hegazy,et al.  A Hybrid AL‐Based System for Site Layout Planning in Construction , 2001 .

[3]  Thomas Stützle,et al.  Improvements on the Ant-System: Introducing the MAX-MIN Ant System , 1997, ICANNGA.

[4]  Ka Chi Lam,et al.  Dynamic construction site layout planning using max-min ant system , 2010 .

[5]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

[6]  Thomas Stützle,et al.  Ant Colony Optimization , 2009, EMO.

[7]  I-Cheng Yeh.  Construction-Site Layout Using Annealed Neural Network , 1995 .

[8]  Min-Yuan Cheng,et al.  ArcSite: enhanced GIS for construction site layout , 1996 .

[9]  Peter E.D. Love,et al.  Genetic search for solving construction site-level unequal-area facility layout problems , 2000 .

[10]  Ka Chi Lam,et al.  The application of the ant colony optimization algorithm to the construction site layout planning problem , 2007 .

[11]  Huang Shabai.  On ant colony algorithm for solving multiobjective optimization problems , 2005 .

[12]  F. A. Meier,et al.  Application of fuzzy decision-making in facilities layout planning , 1996 .

[13]  Deng-Feng Li,et al.  Multiattribute decision making models and methods using intuitionistic fuzzy sets , 2005, J. Comput. Syst. Sci..

[14]  Peter E.D. Love,et al.  Site-Level Facilities Layout Using Genetic Algorithms , 1998 .

[15]  Osama Moselhi,et al.  Computer-aided site layout planning , 2006 .

[16]  Fakhri Karray,et al.  Tools of soft computing as applied to the problem of facilities layout planning , 2000, IEEE Trans. Fuzzy Syst..

[17]  T. Stützle,et al.  MAX-MIN Ant System and local search for the traveling salesman problem , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[18]  Sai On Cheung,et al.  Site pre-cast yard layout arrangement through genetic algorithms , 2002 .

[19]  Lotfi A. Zadeh,et al.  The Concepts of a Linguistic Variable and its Application to Approximate Reasoning , 1975 .

[20]  Krassimir T. Atanassov,et al.  Intuitionistic Fuzzy Sets - Theory and Applications , 1999, Studies in Fuzziness and Soft Computing.

[21]  Ching-Lai Hwang,et al.  Fuzzy Multiple Attribute Decision Making - Methods and Applications , 1992, Lecture Notes in Economics and Mathematical Systems.

[22]  Hisao Ishibuchi,et al.  A simple but powerful heuristic method for generating fuzzy rules from numerical data , 1997, Fuzzy Sets Syst..