Development of a cargo delivery risk management system based on the HAZOP method and the XGBOOST model

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Published: Mar 23, 2025
DOI: https://doi.org/10.47049/2226-1893-2025-1-161-175
Keywords:
transport technologies, risk management, HAZOP method, XGBoost model, machine learning, freight forwarding

Main Article Content

V.M. Piterska
Odesa National Maritime University, Odesa, Ukraine

Abstract

This research aims to develop a risk management system for international freight delivery using the HAZOP method and the XGBoost model. The objectives include analyzing the current freight delivery system and the impact of risks on transport and forwarding operations; justifying the importance of a risk management system; proposing a methodological framework for an effective, risk- oriented delivery system; and developing a risk management system model integrating HAZOP and XGBoost. The research focuses on risk management models and methods within freight delivery systems. Its relevance stems from the need for a methodology addressing the specifics of modern freight delivery systems and optimizing risk management processes. Key findings demonstrate the feasibility of a risk management system for freight delivery and the suitability of the HAZOP method. HAZOP's structured approach to identifying potential hazards and operational problems, focusing on deviations from normal operation, reveals hidden risks missed by other methods. The XGBoost gradient boosting model, a machine learning technique, effectively predicts and quantifies risks, complementing HAZOP's qualitative analysis with quantitative risk assessment using available data. Conclusions indicate that HAZOP identifies risks related to safety (accidents, cargo damage), efficiency (delays, fuel overspending, downtime), regulatory compliance (violations, fines, confiscation), and reputational damage. XGBoost predicts the likelihood of events such as delivery delays, cargo damage, and accidents, quantifies their impact (financial losses, time loss, reputational harm), and identifies risk factors. Combining HAZOP and XGBoost creates a more effective risk management system, leveraging their respective strengths: HAZOP for risk identification and XGBoost for quantitative assessment and prediction within the freight delivery system. The combination of HAZOP and gradient boosting for risk management in the cargo delivery system will allow prioritizing risks by focusing on the most critical risks, developing more effective risk mitigation measures by selecting optimal risk management strategies, improving the risk management system: using the data obtained through gradient boosting for refinement and analysis.

Article Details

How to Cite
Piterska, V. (2025). Development of a cargo delivery risk management system based on the HAZOP method and the XGBOOST model. Herald of the Odessa National Maritime University, (75), 161-175. https://doi.org/10.47049/2226-1893-2025-1-161-175
Issue
No 75 (2025): Herald of the Odessa National Maritime University
Section
Technology and organisation of transportation
Author Biography

V.M. Piterska, Odesa National Maritime University, Odesa, Ukraine

Doctor of Sciences (Engineering), Professor, Professor of the Department of Port Operation and Cargo Handling Technology

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