A multilayer feed-forward neural network (MLFNN) for the resource-constrained project scheduling problem (RCPSP)

Golab, A.; Gooya, E.; Falou, A.; Cabon, M.

Growing Science » Decision Science Letters » A multilayer feed-forward neural network (MLFNN) for the resource-constrained project scheduling problem (RCPSP)

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Decision Science Letters

ISSN 1929-5812 (Online) - ISSN 1929-5804 (Print) Quarterly Publication Volume 11 Issue 4 pp. 407-418 , 2022

A multilayer feed-forward neural network (MLFNN) for the resource-constrained project scheduling problem (RCPSP) Pages 407-418 Download PDF

Authors: Amir Golab, Ehsan Sedgh Gooya, Ayman Al Falou, Mikael Cabon

DOI: 10.5267/j.dsl.2022.7.004

Keywords: Project scheduling, Project management, Artificial neural network, Priority rules, RCPSP, Resource constraint

Abstract: Project management has a fundamental role in national development, industrial development, and economic growth. Schedule management is also one of the knowledge areas of project management, which includes the processes employed to manage the timely completion of the project. This paper deals with the Resource-Constrained Project Scheduling Problem (RCPSP), which is a part of schedule management. The objective of the problem is to optimize and minimize the project duration while constraining the resource quantities during project scheduling. There are two important constraints in this problem, namely resource constraints and precedence relationships of activities during project scheduling. Many methods such as exact, heuristic, and meta-heuristic have been developed by researchers to solve the problem, but there is a lack of investigation of the problem using methods such as neural networks and machine learning. In this article, we develop a multi-layer feed-forward neural network (MLFNN) to solve the standard single- mode RCPSP. The advantage of this method over evolutionary methods or metaheuristics is that it is not necessary to generate numerous solutions or populations. The developed MLFNN learns based on eight project parameters, namely network complexity, resource factor, resource strength, average work per activity, percentage of remaining work, etc., which are calculated at each step of project scheduling, and identified priority rules, which are the outputs of the developed neural network. Therefore, after the learning process, the 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 according to the given project constraints. Finally, we investigate the performance of the presented approach using the standard benchmark problems from PSPLIB.

How to cite this paper

 Golab, A., Gooya, E., Falou, A & Cabon, M. (2022). A multilayer feed-forward neural network (MLFNN) for the resource-constrained project scheduling problem (RCPSP).Decision Science Letters , 11(4), 407-418.

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