Optimization Research: Enhancing the Robustness of Large-Scale Multiobjective Optimization in Construction
Publication: Journal of Construction Engineering and Management
Volume 136, Issue 1
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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Acknowledgments
This material is based upon work supported by the Qatar National Research Fund under Award No. UNSPECIFIEDQNRF-NPRP26-6-7-2. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the writers and do not necessarily reflect the views of the Qatar National Research Fund.
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Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 136 • Issue 1 • January 2010
Pages: 17 - 25
Copyright
© 2010 ASCE.
History
Received: Aug 4, 2008
Accepted: Aug 19, 2009
Published online: Dec 15, 2009
Published in print: Jan 2010
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