cost optimization of projects with fuzzy duration activities using genetic algorithms

The minimum cost is a crucial target of almost all types of construction projects, and it is achieved by efficient scheduling. however, each project is unique and the duration of activities involved in a project often cannot accurately be predicted.  in this research, fuzzy sets were the solution. one prominent point of this research was considering the level of risk acceptance, based on which, crisp durations for activities were attained. in other words, fuzzy scheduling was turned into crisp scheduling. then, a method based on a genetic algorithm was selected to select the operating mode for the smallest project total costs. the last stage of the proposed method was the determination of the fuzzy project cost. simplifications made in this study make it possible to find optimum solutions in complex problems. next, an example of a construction project was used which substantiated that a genetic algorithm with its selected input data (population and generation number) and criterion of selecting surviving chromosomes for the next generation (roulette wheel principle) could deliver reliable outcomes and provide a tool for handling real-world construction projects. furthermore, the performed sensitivity analysis proved that the proposed model is not much sensitive to large variations in the values of the acceptance level of risk. finally, for to validate of the effectiveness of the proposed model, the case study was solved by three widely-used approaches. this comparison (at least 17% improvement in solutions) was a reason for the fact that the presented model is a tool that helps project managers a lot.