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Machine Learning Constraint Solving

Build a constraint resolver in combination with a machine learning agent the solve an advanced employee scheduling problem.

Description of the assignment

  • Companies that schedule their work to be done in shifts, need to solve a common problem: how can we schedule our workers in multiple shifts adhering a couple of constraints and making sure all our shifts are filled in correctly.
  • Hard constraints, e.g. an employee can only do max 2 consecutive nightshifts, are easy to capture, but most of the time there are still multiple possibilities for the planning person to choose from.
  • The planning person has some soft constraints build trough experience in his head that he
    will rely on to pick an ideal schedule among the suggested possibilities.


  • The idea is to capture these soft constraints in a machine learning model and see if it can be used to assist the planning person in making a better decision with minimal intervention.
  • We would build up proof that it is possible and can be put into production.
  • So basically, the project consists of the following tasks:
  • Implement a constraint solver for the hard constraints to solve a simple employee scheduling problem.
  • This results in a couple of possible solutions, which are then labelled using our predefined soft constraints to generate data.
  • A machine learning model will then be trained using that as input and as such learning the soft constraints.
  • Ultimate goal is then to combine the constraint solver and machine learning to provide the best solution among all possible solutions.

What you will gain

• You will learn to prototype and adjust minor errors
• You will learn real-time data captation and processing
• You will explore all kinds of possibilities of different sensors
• You will gain knowledge and experience in designing and producting a machine learning
• That lovely feeling you’ll get knowing your product will in effect contribute to a neater trash
management system. It will effectively be used in our offices!

What you need

• Creativity and the will to succeed
• A motivated personality that can handle setbacks
• You see the bigger picture when it comes to making software and hardware work together
• You want to learn everything about real-time data and stream processing
• You can’t wait to learn a heck of a lot in a relatively limited time period.

Technologies you'll be using
  • Machine Learning
  • Python
  • Java
  • Deep Learning Framework (e.g. TensorFlow, Keras, ...)
  • Apache Kafka
  • Kubernetes
  • Docker
  • Flask - Rest API
Location of your assignment

Veldkant 33B, 2550 Kontich

Your mentor

Kevin Smeyers – Technical lead machine learning ToThePoint

Got what it takes? Apply now!

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