Sustainable Marine Structures

Optimizing Marine Logistics Reliability: An AI-Driven Predictive Maintenance and Cost-Risk Framework

Suleiman Shlash Mohammad

Electronic Marketing and Social Media, Faculty of Economic and Administrative Sciences, Zarqa University, Zarqa 13115, Jordan

Department of Management, Faculty of Business and Communication, INTI International University, Nilai 71800, Malaysia

Badrea Al Oraini

Department of Business Administration, Collage of Business and Economics, Qassim University, Qassim 52571, Saudi Arabia

Hanan Jadallah

Electronic Marketing and Social Media, Faculty of Economic and Administrative Sciences, Zarqa University, Zarqa 13115, Jordan

Asokan Vasudevan

Department of Management, Faculty of Business and Communication, INTI International University, Nilai 71800, Malaysia

Management Faculty, Shinawatra University, Pathum Thani 12160, Thailand

Business Studies Faculty, Wekerle Business School, 1083 Budapest, Hungary

Sultan Alaswad Alenazi

Department of Marketing, College of Administration, King Saud University, Riyadh 11451, Saudi Arabia

DOI: https://doi.org/10.36956/sms.v8i1.2651

Received: 15 August 2025 | Revised: 29 September 2025 | Accepted: 13 October 2025 | Published Online: 12 January 2026

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Abstract

Efficient predictive maintenance is vital for maintaining operational reliability in marine logistics infrastructure, especially within ports and offshore hubs where equipment failure can result in costly downtime and disrupted supply chains. This study introduces an AI-driven predictive maintenance and cost-risk optimization framework that integrates advanced machine learning techniques with Mixed-Integer Linear Programming (MILP) to enable dynamic and data-driven maintenance scheduling. The proposed framework utilizes real-time asset data, including sensor readings, environmental variables, and operational logs collected from 124 marine logistics assets over a six-month monitoring period. Predictive models were developed using the Random Forest algorithm and rigorously validated through time-blocked and grouped cross-validation to prevent data leakage and ensure temporal consistency. The model achieved strong predictive performance, with an AUC of 0.86 and a PR-AUC of 0.71, while calibration reliability was verified using the Brier score and decision curve analysis. The MILP-based optimization component incorporated operational constraints, such as maintenance crew availability, failure probabilities, and environmental stressors, to generate cost-effective maintenance schedules. Implementation of the proposed system resulted in a 21.4% decrease in unplanned downtime, a 16.2% improvement in Mean Time Between Failures (MTBF), and a 13.8% reduction in overall maintenance costs compared with historical benchmarks. This research offers a scalable, interpretable, and data-driven framework for predictive maintenance in complex marine environments, contributing to the advancement of smart port operations, sustainable asset management, and AI-enhanced infrastructure resilience.

Keywords: Artificial Intelligence; Marine Logistics Infrastructure; Cost-Risk Optimization; Smart Ports; Maintenance Scheduling; Port Asset Management

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