Evolutionary algorithm performance evaluation in project time-cost optimization

The time-cost trade-off problem pertains to the assessment of the best method of activity construction so that a project is completed within a given deadline and at least cost. Although several evolutionary-type of algorithms have been reported over the last two decades to solve this NP-hard combinatorial problem, there are not many comparative studies independently evaluating several methods. Such studies can provide support to project managers regarding the selection of the appropriate method. The objective of this work is to comparatively evaluate the performance potential of a number of evolutionary algorithms, each one with its own variations, for the time-cost trade-off problem. The evaluation is based on two measures of effectiveness, the solution quality (accuracy) and the processing time to obtain the solution. The solution is sought via a general purpose commercial optimization software without much interference in algorithm parameter setting and fine-tuning in an attempt to follow the anticipated project manager approach. The investigation has been based on case studies from the literature with varying project size and characteristics. Results indicate that certain structures of genetic algorithms, particle swarm optimization, and differential evolution method present the best performance.