Search-based techniques applied to optimization of project planning for a massive maintenance project

This paper evaluates the use of three different search-based techniques, namely genetic algorithms, hill climbing and simulated annealing, and two problem representations, for planning resource allocation in large massive maintenance projects. In particular, the search-based approach aims to find an optimal or near optimal order in which to allocate work packages to programming teams, in order to minimize the project duration. The approach is validated by an empirical study of a large, commercial Y2K massive maintenance project, which compares these techniques with each other and with a random search (to provide base line comparison data). Results show that an ordering-based genome encoding (with tailored cross over operator) and the genetic algorithm appear to provide the most robust solution, though the hill climbing approach also performs well. The best search technique results reduce the project duration by as much as 50%.