Optimization Research: Enhancing the Robustness of Large-Scale Multiobjective Optimization in Construction |
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Authors: | Amr Kandil Khaled El-Rayes Omar El-Anwar |
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Affiliation: | 1Assistant Professor, Division of Construction Engineering and Management, School of Civil Engineering, Purdue Univ., 550 Stadium Mall Dr., West Lafayette, IN 47907 (corresponding author). E-mail: akandil@purdue.edu 2Associate Professor. Dept. of Civil and Environmental Engineering, Univ. of Illinois at Urbana Champaign, 3127 NCEL, 205 N. Mathews Ave., Urbana, IL 61801. E-mail: elrayes@uiuc.edu 3Assistant Professor, Dept. of Construction Management, Univ. of Washington, Seattle, WA, 98195. E-mail: elanwar@uw.edu
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Abstract: | Many construction planning problems require optimizing multiple and conflicting project objectives such as minimizing construction time and cost while maximizing safety, quality, and sustainability. To enable the optimization of these construction problems, a number of research studies focused on developing multiobjective optimization algorithms (MOAs). The robustness of these algorithms needs further research to ensure an efficient and effective optimization of large-scale real-life construction problems. This paper presents a review of current research efforts in the field of construction multiobjective optimization and two case studies that illustrate methods for enhancing the robustness of MOAs. The first case study utilizes a multiobjective genetic algorithm (MOGA) and an analytical optimization algorithm to optimize the planning of postdisaster temporary housing projects. The second case study utilizes a MOGA and parallel computing to optimize the planning of construction resource utilization in large-scale infrastructure projects. The paper also presents practical recommendations based on the main findings of the analyzed case studies to enhance the robustness of multiobjective optimization in construction engineering and management. |
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Keywords: | Optimization models Parallel processing Resource management Housing Multiple objective analysis Linear analysis Algorithms Construction industry |
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