Optimization research: Enhancing the robustness of large-scale multiobjective optimization in construction

Amr Kandil, Khaled El-Rayes, Omar El-Anwar

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)17-25
Number of pages9
JournalJournal of Construction Engineering and Management
Issue number1
StatePublished - Feb 5 2010


  • Algorithms
  • Housing
  • Linear Analysis
  • Multiple objective analysis
  • Optimization models
  • Parallel processing
  • Resource management

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Industrial relations
  • Strategy and Management


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