Abstracts – Browse Results

Search or browse again.

Click on the titles below to expand the information about each abstract.
Viewing 12 results ...

Al-Ghassani, A M, Kamara, J M, Anumba, C J and Carrillo, P M (2006) Prototype System for Knowledge Problem Definition. Journal of Construction Engineering and Management, 132(05), 516–24.

Baradan, S and Usmen, M A (2006) Comparative Injury and Fatality Risk Analysis of Building Trades. Journal of Construction Engineering and Management, 132(05), 533–9.

Chan, E H, Suen, H C and Chan, C K (2006) MAUT-Based Dispute Resolution Selection Model Prototype for International Construction Projects. Journal of Construction Engineering and Management, 132(05), 444–51.

El-Mashaleh, M, O’Brien, W J and Minchin, R E (2006) Firm Performance and Information Technology Utilization in the Construction Industry. Journal of Construction Engineering and Management, 132(05), 499–507.

Garcia, C, Huebschman, R, Abraham, D M and Bullock, D M (2006) Using GPS to Measure the Impact of Construction Activities on Rural Interstates. Journal of Construction Engineering and Management, 132(05), 508–15.

Hyari, K and El-Rayes, K (2006) Lighting Requirements for Nighttime Highway Construction. Journal of Construction Engineering and Management, 132(05), 435–43.

Kandil, A and El-Rayes, K (2006) Parallel Genetic Algorithms for Optimizing Resource Utilization in Large-Scale Construction Projects. Journal of Construction Engineering and Management, 132(05), 491–8.

  • Type: Journal Article
  • Keywords: Algorithms; Construction management; Computer aided scheduling; Computation; Information technology (IT); Computer models; Contracts;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)0733-9364(2006)132:5(491)
  • Abstract:
    This paper presents the development of a parallel multiobjective genetic algorithm framework to enable an efficient and effective optimization of resource utilization in large-scale construction projects. The framework incorporates a multiobjective optimization module, a global parallel genetic algorithm module, a coarse-grained parallel genetic algorithm module, and a performance evaluation module. The framework is implemented on a cluster of 50 parallel processors and its performance was evaluated using 183 experiments that tested various combinations of construction project sizes, numbers of parallel processors and genetic algorithm setups. The results of these experiments illustrate the new and unique capabilities of the developed parallel genetic algorithm framework in: (1) Enabling an efficient and effective optimization of large-scale construction projects; (2) achieving significant computational time savings by distributing the genetic algorithm computations over a cluster of parallel processors; and (3) requiring a limited and feasible number of parallel processors/computers that can be readily available in construction engineering and management offices.

Lee, E, Lee, H and Harvey, J T (2006) Fast-Track Urban Freeway Rehabilitation with 55-H Weekend Closures: I-710 Long Beach Case Study. Journal of Construction Engineering and Management, 132(05), 465–72.

Lee, S, Peña-Mora, F and Park, M (2006) Reliability and Stability Buffering Approach: Focusing on the Issues of Errors and Changes in Concurrent Design and Construction Projects. Journal of Construction Engineering and Management, 132(05), 452–64.

Uma Maheswari, J, Varghese, K and Sridharan, T (2006) Application of Dependency Structure Matrix for Activity Sequencing in Concurrent Engineering Projects. Journal of Construction Engineering and Management, 132(05), 482–90.

Uwakweh, B O (2006) Motivational Climate of Construction Apprentice. Journal of Construction Engineering and Management, 132(05), 525–32.

Winch, G M and North, S (2006) Critical Space Analysis. Journal of Construction Engineering and Management, 132(05), 473–81.