Artificial Neural Network Approach with Multi-Back Propagation and Training–Testing to Solve the Resource Levelling Problem in PERT-CPM

Inma C Conde (), Luis Antonio Palma Martos, María del Rocío Martínez Torres and Francisco Luis Cumbrera Hernández
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Inma C Conde: Department of Economics, Universidad de Sevilla
Luis Antonio Palma Martos: Department of Economics, Universidad de Sevilla
María del Rocío Martínez Torres: Universidad de Sevilla
Francisco Luis Cumbrera Hernández: Universidad de Sevilla

SN Operations Research Forum, 2025, vol. 6, issue 3, 1-20

Abstract: Abstract Nowadays, due to an improvement in technology and increased competition, project managers have the mandatory task of completing the projects in a foresighted time and with the specified resources. In order to finish the projects without delay, the classical PERT-CPM methodology is being extensively used. In this scenario, two critical problems emerge with no analytical solution: Resource Levelling and Resource Allocation. This paper focuses on the first problem from the novel perspective of Artificial Neural Networks within classic Training–Testing protocol. In order to obtain a reliable dataset for the training process two other targets have been the subject of our research: firstly, the goal was to calibrate quantitatively the efficiency of previous heuristic and metaheuristic approaches to, secondly, train our network with the best results. However, to perform that calibration, research was also conducted to find the optimal efficiency descriptor, beyond the classical Sum of Squares. Using the Principal Component Analysis, an optimal mixed descriptor was found, built from the RIC and Sum of Squares performance descriptors. The research obtained an accuracy of 96,2% regarding the predictions made with a trained neural network. Even if the preliminary task for the Training–Testing protocol is demanding, the reward obtained consists of a quick and comfortable resolution for all the subsequent projects.

Keywords: Project management; PERT-CPM; Resource levelling; Artificial neural networks (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s43069-025-00488-z

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