Multiobjective Approach for Time-Cost Optimization Using a Multi-mode Hybrid Genetic Algorithm

Abstract

This paper presents a hybrid genetic algorithm for the time-cost optimization (TCO) problem. The chromosome representation of the problem is based on random keys. The schedules are constructed using a priority rule in which the priorities are defined by the genetic algorithm. Schedules are constructed using a procedure that generates parameterized active schedules. In construction projects, time and cost are the most important factors to be considered. In this paper, a new hybrid genetic algorithm is developed for the optimization of the two objectives time and cost. The approach was developed in VBA and applied to test problems reported on the literature and compared with other approaches. The results indicate that this approach could assist decision-makers to obtain good solutions for project duration and total cost.

An Innovative Methodology for Online Surrogate-Based Model Updating of Historic Buildings Using Monitoring Data

“…genetic algorithms, particle swarm optimization, bees algorithms, etc). Despite being computationally demanding, evolutionary algorithms have superior capabilities to find global extrema under the presence of non-linearities, damping, measurement errors, as well as a large number of updating parameters [19]. Some application examples of these techniques to historic masonry structures can be found in the literature.…”