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