Experimental Design for Genetic Algorithm Simulated Annealing for Time Cost Trade-off Problems

Abstract

Optimum solution of time cost trade-off (TCT) problem has significant importance for construction sector as it maximizes the profit of the project. As this is the case, numerous solution techniques are adopted for the optimum solution of TCT. Meta-heuristics are prevalent techniques for the adaptation of optimum solution of TCT. Meta-heuristic algorithms are problem independent algorithms; however their input parameters are sensitive to the problem type and are not immutable. Erroneous assignment of input parameters may abate the convergence to the optimum solution or even prevent the convergence to the optimum. In order to improve input parameters of the hybrid meta-heuristic algorithm; Genetic Algorithm with Simulated Annealing (GASA) an experimental design is implemented on an 18-Activity project. The correlation between the parameters and the sensitivity of the input parameters are revealed.

Keywords

• Time Cost Trade-off, Genetic Algorithm, Simulated Annealing, Experimental Design

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