Simulation-Driven Predictive AI for Printer Repair Services: A Proactive Ticket Resolution Approach

DOI

10.2991/978-94-6239-711-8_17How to use a DOI?

Keywords

Artificial Intelligence; Customer Service; Classification; Pre-diction; Machine Learning; Printer Maintenance

Abstract

This study presents a predictive, artificial intelligence–driven ticket resolution framework aimed at improving customer service efficiency within the printer maintenance domain, a critical technical sup-port area for both organizational and individual users. Owing to the limited availability of reliable, publicly accessible real-world maintenance data, the proposed system is developed and evaluated using a simulation-based approach. A synthetic dataset comprising 150 maintenance cases and 10 relevant attributes was generated to realistically model common printer service scenarios. Twelve machine learning classification algorithms were implemented and systematically evaluated using two distinct random states (20 and 42) to ensure robustness and reproducibility. Model performance was assessed using accuracy and F-score metrics. The experimental results demonstrate that the Light Gradient Boosting Machine (LightGBM) classifier outperformed the other models, achieving an accuracy of 0.70 and an F-score of 0.7033. These findings confirm the feasibility and effectiveness of simulation-driven predictive modeling for proactive ticket resolution in contexts where real-world data are unavailable or incomplete. The study underscores the potential of artificial intelligence to transition customer support systems from traditional reactive mechanisms toward proactive, intelligent service management solutions in operational maintenance environments.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Samia Zouaoui
PY  - 2026
DA  - 2026/06/24
TI  - Simulation-Driven Predictive AI for Printer Repair Services: A Proactive Ticket Resolution Approach
BT  - Proceedings of the International Conference on Artificial Intelligence Applications in Business Administration in MENA Region (ICAIABA 2026)
PB  - Atlantis Press
SP  - 173
EP  - 182
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-711-8_17
DO  - 10.2991/978-94-6239-711-8_17
ID  - Zouaoui2026
ER  -