Improved genetic algorithm for resource-constrained scheduling of large projects

Abstract

The generalized model of the resource-constrained project scheduling problem (RCPSP) is valuable because it can be incorporated into the advanced computational methods of commercial project management software for practical applications. A construction schedule generated by most commercial project management programs does not guarantee its optimality when the resources are limited. This paper presents an improved elitist genetic algorithm (GA) for resource-constrained scheduling of large projects. The proposed algorithm allocates multiple renewable resources to activities of a single large-sized project to achieve the objective of minimizing the project duration. A permutation-based decoding procedure is developed using the improved parallel schedule generation scheme. A new parameter, named transformation power, is created in the transformation method of the improved algorithm to ensure that the individual selection process performs correctly. Extensive computational results using a standard set of large-sized multiple resource-constrained project scheduling problems are presented to demonstrate the performance and accuracy of the algorithm.