A novel learning algorithm based on a multi-agent structure for solving multi-mode resource-constrained project scheduling problem

Abstract

The resource-constrained project scheduling problem (RCPSP) includes activities which have to be scheduled due to precedence and resource restrictions such that an objective is satisfied. There are several variants of this problem currently, and also different objectives are considered with regards to the specific applications. This paper tries to introduce a new multi-agent learning algorithm (MALA) for solving the multi-mode resource-constrained project scheduling problem (MMRCPSP), in which the activities of the project can be performed in multiple execution modes. This work aims to minimize the total project duration which is referred to its makespan. The experimental results show that our method is a new one for this specific problem and can outperform other algorithms in different areas. © 2012 Springer Science+Business Media.