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Alkriz, K and Mangin, J-C (2005) A new model for optimizing the location of cranes and construction facilities using genetic algorithms. In: Khosrowshahi, F (Ed.), Proceedings 21st Annual ARCOM Conference, 7-9 September 2005, London, UK. Association of Researchers in Construction Management, Vol. 2, 981–91.

  • Type: Conference Proceedings
  • Keywords: construction site layout; genetic algorithms; modelling; optimisation; productivity
  • ISBN/ISSN: 0 902896 93 8
  • URL: http://www.arcom.ac.uk/-docs/proceedings/ar2005-0981-0991_Alkriz_and_Mangin.pdf
  • Abstract:
    In the field of construction, the positioning of cranes and facilities within construction site is a very important phase for construction companies with major stakes on cost and duration of any construction project. The application of a quantitative approach to determine the optimum positions for cranes and facilities is highly desirable for construction site planning. Actually, this task tends to be carried out manually by experienced engineers during preparation and organization phases of construction. This operation is complex and difficult to achieve because of the complexity of knowledge and the considerable amount of facilities used and the interactions between them. Sometimes there is a great risk when implementing poor choices, which are expensive for the company; with wasted time and substantial loss of productivity. Therefore, overcosts could be the result of non relevant choices for locating facilities. This paper aims to develop an optimisation system and decision support tool, based on Genetic Algorithms (GAs) to determine the optimal position for the cranes and the facilities in construction sites. This tool depends on spatial modelling of the construction site, with site's elements and optimisation method based on genetic algorithms. Several criteria of evaluation are proposed in order to assess the performance of different solutions. These criteria can be: the total hook travel times of cranes, the severity of conflicts between cranes and the balancing workloads of cranes. Optimisation results are shown to illustrate the proposed model and appropriate conclusions are drawn.