Resource leveling in construction by genetic algorithm-based optimization and its decision support system application

Abstract

Traditional analytical and heuristic approaches are inefficient and inflexible when solving construction resource leveling problems. A computational optimization technique, genetic algorithms (GAs), was employed in this study to overcome drawbacks of traditional construction resource leveling algorithms. The proposed algorithm can effectively provide the optimal or near-optimal combination of multiple construction resources, as well as starting and finishing dates of activities subjected to the objective of resource leveling. Furthermore, a prototype of a decision support system (DSS) for construction resource leveling was also developed. Construction planners can interact with the system to carry out ad hoc analysis through “what-if” queries.