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Prediction of multiproject resource conflict risk via an artificial neural network.

Authors :
Bai, Libiao
Wang, Zhiguo
Wang, Hailing
Huang, Ning
Shi, Huijing
Source :
Engineering Construction & Architectural Management (09699988); 2021, Vol. 28 Issue 10, p2857-2883, 27p
Publication Year :
2021

Abstract

Purpose: Inadequate balancing of resources often results in resource conflict in the multiproject management process. Past research has focused on how to allocate a small amount of resources optimally but has scarcely explored how to foresee multiproject resource conflict risk in advance. The purpose of this study is to address this knowledge gap by developing a model to predict multiproject resource conflict risk. Design/methodology/approach: A fuzzy comprehensive evaluation method is used to transform subjective judgments into quantitative information, based on which an evaluation index system for multiproject resource conflict risk that focuses on the interdependence of multiple project resources is proposed. An artificial neural network (ANN) model combined with this system is proposed to predict the comprehensive risk score that can describe the severity of risk. Findings: Accurately predicting multiproject resource conflict risks in advance can reduce the risk to the organization and increase the probability of achieving the project objectives. The ANN model developed in this paper by the authors can capture the essential components of the underlying nonlinear relevance and is capable of predicting risk appropriately. Originality/value: The authors explored the prediction of the risks associated with multiproject resource conflicts, which is important for improving the success rate of projects but has received limited attention in the past. The authors established an evaluation index system for these risks considering the interdependence among project resources to describe the underlying factors that contribute to resource conflict risks. The authors proposed an effective model to forecast the risk of multiproject resource conflicts using an ANN. The model can effectively predict complex phenomena with complicated and highly nonlinear performance functions and solve problems with many random variables. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09699988
Volume :
28
Issue :
10
Database :
Complementary Index
Journal :
Engineering Construction & Architectural Management (09699988)
Publication Type :
Academic Journal
Accession number :
153442760
Full Text :
https://doi.org/10.1108/ECAM-03-2020-0201