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Jefferies, M, Gameson, R and Rowlinson, S (2002) Critical success factors of the BOOT procurement system: reflections from the Stadium Australia case study. Engineering, Construction and Architectural Management, 9(04), 352–61.

Love, P E D, Holt, G D and Li, H (2002) Triangulation in construction management research. Engineering, Construction and Architectural Management, 9(04), 294–303.

Mrad, F, Abdul-Malak, M A, Sadek, S and Khudr, Z (2002) Automated excavation in construction using robotics trajectory and envelop generation. Engineering, Construction and Architectural Management, 9(04), 325–35.

Potts, K and Wall, M (2002) Managing the commissioning of building services. Engineering, Construction and Architectural Management, 9(04), 336–44.

Psilander, K (2002) Axiomatic design in the customizing home building industry. Engineering, Construction and Architectural Management, 9(04), 318–24.

Ugwu, O O and Tah, H M (2002) Development and application of a hybrid genetic algorithm for resource optimization and management. Engineering, Construction and Architectural Management, 9(04), 304–17.

  • Type: Journal Article
  • Keywords: combinatorial optimization; decision support systems; distributed project management; genetic algorithms; resource optimization
  • ISBN/ISSN: 0969-9988
  • URL: http://www.ingentaconnect.com/search/expand?pub=infobike://bsc/ecam/2002/00000009/00000004/art00003&unc=
  • Abstract:
    Resource selection/optimization problems are often characterized by two related problems: numerical function and combinatorial optimization. Although techniques ranging from classical mathematical programming to knowledge-based expert systems (KBESs) have been applied to solve the function optimization problem, there still exists the need for improved solution techniques in solving the combinatorial optimization. This paper reports an exploratory work that investigates the integration of genetic algorithms (GAs) with organizational databases to solve the combinatorial problem in resource optimization and management. The solution strategy involved using two levels of knowledge (declarative and procedural) to address the problems of numerical function, and combinatorial optimization of resources. The research shows that GAs can be effectively integrated into the evolving decision support systems (DSSs) for resource optimization and management, and that integrating a hybrid GA that incorporates resource economic and productivity factors, would facilitate the development of a more robust DSS. This helps to overcome the major limitations of current optimization techniques such as linear programming and monolithic techniques such as the KBES. The results also highlighted that GA exhibits the chaotic characteristics that are often observed in other complex non-linear dynamic systems. The empirical results are discussed, and some recommendations given on how to achieve improved results in adapting GAs for decision support in the architecture, engineering and construction (AEC) sector

Wild, A (2002) The unmanageability of construction and the theoretical psycho-social dynamics of projects. Engineering, Construction and Architectural Management, 9(04).