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A machine learning driven decision support system for evaluating port performance: development and validation

Leonardo Leoni, Xiaotian Xie, Guoqing Zhao Orcid Logo, Yi Wang, Filippo De Carlo

Journal of Decision Systems, Volume: 35, Issue: 1

Swansea University Author: Guoqing Zhao Orcid Logo

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DOI (Published version): 10.1080/12460125.2026.2682227

Abstract

Limited research has examined how ports’ big data analytics capability (BDAC) is associated with operational and sustainable performance. In response, this study develops and validates a decision support system (DSS) that integrates expert judgements, fuzzy set theory, unsupervised machine learning...

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Published in: Journal of Decision Systems
ISSN: 1246-0125 2116-7052
Published: Informa UK Limited 2026
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URI: https://cronfa.swan.ac.uk/Record/cronfa71987
Abstract: Limited research has examined how ports’ big data analytics capability (BDAC) is associated with operational and sustainable performance. In response, this study develops and validates a decision support system (DSS) that integrates expert judgements, fuzzy set theory, unsupervised machine learning (ML), Decision Trees, and Bayesian Network analysis. Data were collected through a Likert-scale questionnaire completed by 158 respondents from 40 major ports. The responses were aggregated using an improved Similarity Aggregation Method, and K-Means clustering was applied to classify ports into performance groups. Decision Trees were then developed to identify performance clusters and key improvement areas, while a Bayesian Network was used to explore relationships among BDAC, port operational performance, and port sustainable performance. The results indicate that ports with stronger BDAC generally achieve better operational and sustainable performance, although other contextual factors may also play important roles.
Keywords: Big data analytics capability; port operations; port management; decision-making
College: Faculty of Humanities and Social Sciences
Issue: 1

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