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Bashford, H H, Walsh, K D and Sawhney, A (2005) Production System Loading–Cycle Time Relationship in Residential Construction. Journal of Construction Engineering and Management, 131(01), 15–22.

Chen, Z, Li, H and Wong, C T C (2005) {[}EnvironalPlanning{]}: Analytic Network Process Model for Environmentally Conscious Construction Planning. Journal of Construction Engineering and Management, 131(01), 92–101.

Chester, M and Hendrickson, C (2005) Cost Impacts, Scheduling Impacts, and the Claims Process during Construction. Journal of Construction Engineering and Management, 131(01), 102–7.

Dzeng, R J, Tserng, H P and Wang, W C (2005) Automating Schedule Review for Expressway Construction. Journal of Construction Engineering and Management, 131(01), 127–36.

Elazouni, A M, Ali, A E and Abdel-Razek, R H (2005) Estimating the Acceptability of New Formwork Systems Using Neural Networks. Journal of Construction Engineering and Management, 131(01), 33–41.

Horman, M J and Kenley, R (2005) Quantifying Levels of Wasted Time in Construction with Meta-Analysis. Journal of Construction Engineering and Management, 131(01), 52–61.

Kaiser, M J, Pulsipher, A G and Byrd, R C (2005) Cost of Abrasive Cutting in Decommissioning Operations in the Gulf of Mexico. Journal of Construction Engineering and Management, 131(01), 137–48.

Kaiser, M J, Pulsipher, A G and Martin, J (2005) Cost of Site Clearance and Verification Operations in the Gulf of Mexico. Journal of Construction Engineering and Management, 131(01), 117–26.

Oliveros, A V O and Fayek, A R (2005) Fuzzy Logic Approach for Activity Delay Analysis and Schedule Updating. Journal of Construction Engineering and Management, 131(01), 42–51.

Singh, D and Tiong, R L K (2005) A Fuzzy Decision Framework for Contractor Selection. Journal of Construction Engineering and Management, 131(01), 62–70.

Tatum, C B (2005) Building Better: Technical Support for Construction. Journal of Construction Engineering and Management, 131(01), 23–32.

Yiu, C Y, Ho, H K, Lo, S M and Hu, B Q (2005) Performance Evaluation for Cost Estimators by Reliability Interval Method. Journal of Construction Engineering and Management, 131(01), 108–16.

Zhang, X (2005) Critical Success Factors for Public–Private Partnerships in Infrastructure Development. Journal of Construction Engineering and Management, 131(01), 3–14.

Zhang, X (2005) Paving the Way for Public–Private Partnerships in Infrastructure Development. Journal of Construction Engineering and Management, 131(01), 71–80.

Zheng, D X M, Ng, S T and Kumaraswamy, M M (2005) Applying Pareto Ranking and Niche Formation to Genetic Algorithm-Based Multiobjective Time–Cost Optimization. Journal of Construction Engineering and Management, 131(01), 81–91.

  • Type: Journal Article
  • Keywords: Adaptive systems; Construction costs; Algorithms; Scheduling; Optimization; Construction management; Time factors;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)0733-9364(2005)131:1(81)
  • Abstract:
    Time–cost optimization (TCO) is one of the greatest challenges in construction project planning and control, since the optimization of either time or cost, would usually be at the expense of the other. Although the TCO problem has been extensively examined, many research studies only focused on minimizing the total cost for an early completion. This does not necessarily convey any reward to the contractor. However, with the increasing popularity of alternative project delivery systems, clients and contractors are more concerned about the combined benefits and opportunities of early completion as well as cost savings. In this paper, a genetic algorithms ( GAs ) -driven multiobjective model for TCO is proposed. The model integrates the adaptive weight to balance the priority of each objective according to the performance of the previous “generation.” In addition, the model incorporates Pareto ranking as a selection criterion and the niche formation techniques to improve popularity diversity. Based on the proposed framework, a prototype system has been developed in Microsoft Project for testing with a medium-sized project. The results indicate that greater robustness can be attained by the introduction of adaptive weight approach, Pareto ranking, and niche formation to the GA -based multiobjective TCO model.