Egg grading planning tool – Sanovo
Overview
| Company Name / Department | Sanovo Technology Netherlands |
| Contact Person |
G. Heinen |
| Location | Aalten |
| Optional remote work | Yes, officially 3 office, 2 at home, but for this thesis project we could agree on another distribution |
| Travel expenses (own account or reimbursed by the company) |
Incl. in compensation for home to work travel Travel for the assignment to e.g. a customer is fully reimbursed |
| Housing arranged by company | No |
| Housing expenses (how much per month, own account or subsidized by the company) | No |
| Internship compensation | €500 per month |
| Study program | |
| ESCF community | |
| Start date | September 2025 |
Company Description
Sanovo is a global leader of developing equipment for processing and handling of eggs. In the Netherlands the development and production of the egg grading machines takes places. The main process of this machine is to grade and pack eggs based on characteristics like weight, shell color, and quality. From simple sensors to AI and deep learning is used to measure those characteristics.
Project Description
Each day, a grading station must fulfill multiple orders (e.g., size L brown eggs in a specific carton type) using a limited set of egg batches from different poultry houses. Each packaging lane on the machine can only run one type of carton at a time, and switching between carton types takes time and introduces risk of errors. To maximize efficiency, it’s important to plan which egg batches to offer and how to configure the machine throughout the day.
The goal of this project is to develop an algorithm that creates the most efficient daily production schedule based on available egg batches and a list of known orders. The algorithm should minimize changeovers, reduce egg waste, and ensure full order fulfillment. The student will analyze the current process, model key constraints, and build a working prototype to test the approach.
Goals of the project
Design an optimization algorithm that plans the sequence of egg batches and machine configurations.
The algorithm should aim to minimize setup time, reduce egg waste, and maximize order fulfillment efficiency.
Implement a proof-of-concept tool or simulation that demonstrates the effectiveness of the algorithm.
Deliverables
- Algorithm that plans the sequence of egg batches and machine configurations.
- Proof-of-concept tool or simulation that demonstrates the effectiveness of the algorithm.
- Documented report of findings, (possible) risks and outstanding challenges.
Knowledge
- Programming experience (e.g., Python, R, MATLAB, …)
- Analytical thinking and problem-solving ability.
- A background in optimization, data science, or industrial engineering is recommended.
More information: escf@tue.nl
