Investigating the performance of an artificial neural network for solving the resource constrained project scheduling problem (RCPSP)

Abstract

Project management plays a fundamental role in national development and economic improvement. Schedule management is also one of the knowledge areas of project management. This paper deals with the Resource-Constrained Project Scheduling Problem (RCPSP), which is a part of schedule management. The objective is to optimize and minimize the project duration while constraining the amount of resources during project scheduling. In this problem, resource constraints and precedence relationships of activities are known as important constraints for project scheduling. Many methods such as exact, heuristic, and meta-heuristic have been proposed by researchers to solve the problem, but there is a lack of investigation of the problem using new methods such as neural networks and machine learning. In this context, we investigate the function of a feed-forward neural network on the standard single-mode RCPSP. The artificial neural network learns based on the scheduling level characterized by parameters, namely network complexity, resource factor, resource strength, etc., calculated at each stage of project scheduling and identified priority rules. Therefore, after the learning process, the developed artificial neural network can automatically select an appropriate priority rule to filter out an unscheduled activity from the list of eligible activities and schedule all activities of the project in accordance with the specified project constraints.