Working Student (f/m/d) - Reinforcement Learning in Pallet Building Optimization
Ver: 159
Dia de atualização: 22-10-2024
Localização: Hallbergmoos Bavaria
Categoria: R & D
Indústria: IT Services IT Consulting Software Development Business Consulting Services
Tipo de empregos: Full-time
Conteúdo do emprego
We help the world run betterOur company culture is focused on helping our employees enable innovation by building breakthroughs together. How? We focus every day on building the foundation for tomorrow and creating a workplace that embraces differences, values flexibility, and is aligned to our purpose-driven and future-focused work. We offer a highly collaborative, caring team environment with a strong focus on learning and development, recognition for your individual contributions, and a variety of benefit options for you to choose from. Apply now!What You’ll DoPallet building is a fundamental optimization problem in logistics. Given a list of unpacked items, the main objective is to minimize the number of required pallets to ship all items.The goal of this project is to evaluate Reinforcement Learning approaches in the context of a pallet building metaheuristic optimizer. Although involved local search operators in the existing metaheuristic find an adequate solution in most cases, complex scenarios result in a large solution space which cannot be fully exploited in a feasible amount of time.The candidate will evaluate both from a theoretical and practical perspective whether and how Reinforcement Learning can improve the solution quality of the existing pallet building optimizer.What You Bring- Student (f/m/d) at a university or a university of applied sciences
- Preferred fields of study: Mathematics, Informatics, Operations Research
- Computer skills: Python, C++ (or the willingness to learn it)
- Language skills: fluent in English, German is a plus
- Soft skills: You are a technical-affine person and a good communicator.
- Others: Experience with Reinforcement Learning concepts is a plus
Data limite: 06-12-2024
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