A Recommendation System-Based Framework for Enhancing Human-Machine Collaboration in Industrial Timetabling Rescheduling: Application in Preventive Maintenance

TL;DR

This paper introduces a recommendation system framework utilizing AI-driven planning to improve human-machine collaboration in industrial timetabling, especially for rescheduling during unexpected events, demonstrated through a real-world preventive maintenance case.

Contribution

It presents a novel AI-based framework for effective rescheduling in industrial timetabling, integrating human-machine collaboration and heuristic evaluation for real-world applications.

Findings

Identified heuristics balancing solution quality and computation time.

Demonstrated the framework's effectiveness in a real-world preventive maintenance scenario.

Showcased the complete rescheduling process through a practical use case.

Abstract

Industrial timetabling is a critical task for decision-makers across various sectors to ensure efficient system operation. In real-world settings, it remains challenging because unexpected events often disrupt execution. When such events arise, effective rescheduling and collaboration between humans and machines becomes essential. This paper presents a recommendation system-based framework for handling rescheduling challenges, built on Timefold, a powerful AI-driven planning engine. Our experimental study evaluates nine instances inspired by a realworld preventive maintenance use case, aiming to identify the heuristic that best balances solution quality and computing time to support near-optimal decisionmaking when rescheduling is required due to unexpected events during operational days. Finally, we illustrate the complete process of our recommendation system through a simple use case.