Performance of global optimization models for dynamic site layout planning of construction projects |
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| Affiliation: | 1. Department of Civil Engineering, Santa Clara University, Santa Clara, CA, United States;2. Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States;1. DPR Construction, 2941 Fairview Park Dr., Suite 600, Falls Church, VA 22042, United States;2. Civil and Environment Engineering and (by courtesy) Computer Science, Center for Integrated Facility Engineering, Stanford University, Room 297, The Jerry Yang & Akiko Yamazaki Environment & Energy Building, United States;3. Center for Integrated Facility Engineering, Department of Civil and Environmental Engineering, Stanford University, Room 293, The Jerry Yang & Akiko Yamazaki Environment & Energy Building, Stanford, CA 94305, United States;4. Civil and Environment Engineering, Collaboratory for Research and Global Projects, Stanford University, Room 241, The Jerry Yang & Akiko Yamazaki Environment & Energy Building, Stanford, CA 94305, United States;1. School of Civil Engineering & Mechanics, Huazhong University of Science & Technology, Wuhan, Hubei Province, China;2. School of Investment & Construction Management, Dongbei University of Finance & Economics, Dalian, Liaoning Province, China;3. College of Civil Engineering, Huaqiao University, Xiamen, Fujian Province, China;1. Business School, Sichuan University, Chengdu 610064, PR China;2. State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610064, PR China;3. Decision Sciences Department, LeBow College of Business, Drexel University, Philadelphia, PA 19104, USA |
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| Abstract: | Dynamic construction site layout planning is a complex optimization problem that is characterized by nonlinear objectives and constraints, which impose great challenges in obtaining global and feasible solutions. This paper presents and compares between two global optimization models of dynamic site layout planning that were developed to overcome the limitation of previous models in the literature. The first model utilizes Genetic Algorithms (GA) while the second model utilizes Approximate Dynamic Programming (ADP). The performance of these two optimization models is analyzed in terms of the effectiveness of reaching optimum solutions and the efficiency of reducing the computational time. This analysis is performed using a designed set of problems of dynamic site layout planning with changing size and complexity. It was found that ADP outperformed GAs in terms of effectiveness and efficiency. However, GAs still prove to be a viable optimization tool because of its simplicity and multi-objective optimization capabilities. |
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