A development of dynamic construction site layout planning decision-making system

Abstract

An optimal construction site layout planning (CSLP) can reduce the transportation flows, moving, reloading, and enhancing the services of the construction work to improve the work productivity and thus the costs of a project. The previous studies of CSLP centralized on the equal-area and static site layout planning, which is idealized but practical unequal-area and dynamic planning studies are limited. In order to enhance the knowledge and practices of solving unequal-area and dynamic problems, the CSLP decision-making system, which can handle both unequal-area and dynamic CSLP problems, using ant colony optimization (ACO) algorithms is proposed in the study. The proposed CSLP decision-making system consists of four stages, namely, input, design, evaluation & selection and output stages. In the input stage, it consists of site condition, schedule planning and construction objectives. In the design stage, both single objective optimization (SOO) model using max-min ant system (MMAS) and multiple objective optimization (MOO) model using Pareto ACO algorithm are employed. In the evaluation & selection stage, the proposed CSLP decision makingsystem integrated the modified intuitionistic fuzzy TOPSIS method to evaluate and select the best site layout from the generated construction site layout alternatives by the SOO model and the MOO model. The feasibility and effectiveness of the proposed CSLP decision-making system were tested and verified by two projects-one was office building and the other was residential building. The results showed that the proposed CSLP decision-making system can select a good site layout, which reduced the project costs significantly by comparing with the original site layouts. The limitations, advantages of the proposed CSLP decision making system and further studies to perfect the proposed CSLP decision making system are recommended. Keywords: Construction site layout planning, Decision-making system, ACO algorithms, Intuitionistic fuzzy TOPSIS.

Date of Award	2 Oct 2008
Original language	English
Awarding Institution	City University of Hong Kong
Supervisor	Ka Chi LAM (Supervisor)