This paper presents an investigation of the applicability of a genetic approach for solving the construction site layout problem. This problem involves coordinating the use of limited site space to accommodate temporary facilities so that transportation cost of materials is minimized. The layout problem considered in this paper is characterized by affinity weights used to model transportation costs between facilities and by geometric constraints that limit their relative positions on site. The proposed genetic algorithm generates an initial population of layouts through a sequence of mutation operations and evolves the layouts of this population through a sequence of genetic operations aiming at finding an optimal layout. The paper concludes with examples illustrating the strength and limitations of the proposed algorithm in the cases of ͑1͒ loosely versus tightly constrained layouts with equal levels of interaction between facilities; ͑2͒ loosely versus tightly packed layouts with variable levels of interactions between facilities; and ͑3͒ loosely versus tightly constrained layouts. In most problems considered where the total-objects-to-site-area ratio did not exceed 60%, the algorithm returned close to optimal solutions in a reasonable time.
Construction site layout has been recognized as an important activity in construction site planning by field practitioners and researchers alike. This problem involves coordinating the use of limited space to accommodate temporary facilities (such as fabrication shops, trailers, materials or equipment) so that transportation costs of resources are minimized. The layout problem considered in this paper is a static layout problem characterized by affinity weights used to model transportation costs between facilities and by geometric constraints between relative positions of facilities on site. This paper presents an investigation of applying an evolutionary approach to optimally solve the aforementioned layout problem. The proposed algorithm is two-phases: an initialization phase that generates an initial population of layouts through a sequence of mutation operations, and a reproduction phase that evolve the layouts generated in phase one through a sequence of genetic operations aiming at finding an optimal layout. The paper concludes with a number of examples illustrating the strength and limitations of the proposed approach.
Parallel genetic algorithms techniques have been used in a variety of computer engineering and science areas. This paper presents a parallel genetic algorithm to solve the site layout problem with unequal-size and constrained facilities. The problem involves coordinating the use of limited space to accommodate temporary facilities subject to geometric constraints. The problem is characterised by affinity weights used to model transportation costs between facilities, and by geometric constraints between relative positions of facilities on site. The algorithm is parallelised based on a message passing SPMD architecture using parallel search and chromosomes migration. The algorithm is tested on a variety of layout problems to illustrate its performance. In specific, in the case of: (1) loosely versus tightly constrained layouts with equal levels of interaction between facilities, (2) loosely versus tightly packed layouts with variable levels of interactions between facilities, and (3) loosely versus tightly constrained layouts. Favorable results are reported.
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