Efficient workforce scheduling and workload management are critical for any modern manufacturing process. LeMur sought a system that could generate optimized production plans, balancing workload distribution while respecting the unique constraints of their spiral binding operations—where manual interventions and asynchronous machine operations add complexity.
Our team set out to design a smart scheduler capable of suggesting optimal machine and operator schedules, ensuring balanced distribution of manual tasks, and delivering scalable and flexible planning over an extended time horizon—going beyond the company's current day-by-day planning approach.
To build an effective solution, we first developed a deep understanding of LeMur's internal processes, defining key concepts like:
We identified hard constraints (e.g., max simultaneous manual operations) and incorporated company-specific logic such as active/inactive cycles, partial/complete cycles, and machine capacity variations.
The core scheduling problem falls under the Job Shop Problem (JSP) category, which is computationally complex. We chose a hybrid approach combining:
Our hybrid solver delivers:
Tests showed our scheduler better reflects actual workload realities, highlighting areas where the current planning might underestimate operational demands.
To make the system user-friendly and practical:
The system paves the way for:
@misc{decarlo2025lemur,
title={LeMur Scheduler: An Innovative Approach to Spiral Binding Planning},
author={De Carlo, Andrea and Cazzola, Luca and Lorenzi, Alessandro and Sperandio, Luca and Cavicchini, Davide and Poiana, Emanuele and Vento Maddonni, Silvano},
year={2025},
institution={University of Trento},
type={Industrial AI Challenge Project},
note={Winner of Industrial AI Challenge 2025}
}