Heuristic applications of the RCPSP in large-scale industrial project planning H/F
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The Optimization group at Assystem’s Data & Digital Factory currently aims to provide its clients with a digital project management tool that can aid in decision-making.

The focus is on the well-known Resource-Constrained Project Scheduling Problems (RCPSP) for which numerous methodologies (heuristics and meta-heuristics) have been proposed in the research domain.

In the context of project planning of large-scale projects, the client objectives can be multifold. Delays in projects are undesirable, can have serious financial consequences, and thus requires optimization of the project plans while respecting the constraints.

The optimized / feasible planning should highlight or remove bottlenecks, respect project constraints, reduce delays, and offer insights to the client to take well-informed decisions.

Assystem Data & Digital Factory aims to offer such a solution that uses robust algorithms to resolve the underlying RCPSP problems while also allowing expert inputs.

The objective is to provide the clients the accurate information without incoherencies and conflicts, flexibility and ease of use while improving the computational efficiency.

Internship Description

The focus of the proposed internship is to take part in the research and development of the solution. The candidate will be required to research possible heuristic and meta-heuristic approaches for the RCPSP and should be able to translate the theoretical knowledge gained over the course of the research into development of practical, feasible and efficient solutions.

Apart from the main objectives, the candidate will also be encouraged to take active part in the current development of the project that can include :

  • Proposing / developing informative Key Performance Indicators (KPIs).
  • Software testing : white-box and black-box testing.

  • Explore ideas to improve computational efficiency
  • Debugging
  • Most importantly, you are free to invent your journey with your ideas and personality in this R&D.

    Skills and Knowledge

    1. Studying for Bachelor or Master degree in Engineering

    2. Strong research and analytical skills

    3. Strong knowledge in combinatorial optimization topics.

    4. Knowledge of applied mathematics, meta-heuristics, scheduling algorithms and object-oriented programming is a plus.

    5. Other skills : Python, Git.

    6. Fluency in English.

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