Dynamic Multi-objective Layout Planning of Construction Sites. (2017)

• Record Type: Journal Article
• Authors: Farmakis, Panagiotis M.; Chassiakos, Athanasios P.
• Is Part Of: Procedia engineering. Volume 196(2017)
• Page Start: 674
• Page End: 681
• Publication Date: 2017
• Subjects: construction site -- layout planning -- genetic algorithms -- optimization -- safety; Engineering -- Congresses; Engineering -- Periodicals; Engineering; Conference proceedings; Periodicals; 620.005
• DOI: 10.1016/j.proeng.2017.08.057
• Languages: English
• ISSNs: 1877-7058

Abstract

Abstract: Efficient layout planning of a construction site is fundamental for successful project undertaking as it enhances both productivity and safety of operations. The site layout planning problem is a complex combinatorial optimization problem involving multiple objectives and it grows significantly in size as the numbers of facilities and constraints increase. In addition, as construction evolves, the site layout may need to be dynamically reorganized at various schedule intervals to accommodate operational needs. As opposed to considering only productivity issues, this paper presents an approach for site layout planning that considers, in addition, safety and environmental issues resulting from the proximity or remoteness of certain facilities to others or to existing eco-sensitive assets (e.g., rivers). The optimization model developed includes the necessary constraints resulting from physical and operational limitations and can additionally model given priorities or preferences set by the project or site manager depending on particularities of the project or the construction site. The model is developed to handle more than one project phases which may optimally require facility relocation (especially in projects where the site layout "is moving" regularly (e.g., highway construction). The inclusion of several project phases and site reorganization requires solving a significantly larger problem than considering a static layout, if full optimization is done. For this reason, the development integrates the required robust search objective with the optimization capabilities of genetic algorithms (GAs). The model has been tested on several case studies considering both static and dynamic site layout planning and results indicate that it provides rational solutions, in response to decision parameters and problem constraints, and that dynamic modeling develops more effective layout planning than a static one.