Γ-opt

Γ-opt

Group of Applied Mathematical Modelling and Optimisation

Aalto University

About us

The Γ-opt (reads gamma-opt) research group is a part of the Systems Analysis Laboratory in the Department of Mathematics and Systems Analysis at Aalto University.

We develop research in the fields of Operations Research and Management Science, with emphasis on Optimisation, Mathematical Programming, and applications related to Production Systems Planning and Supply Chain Management.

If you are interested in positions, collaborations or exchange opportunities, please contact Fabricio Oliveira.

Research topics

production_system
Production systems & supply chain management

  • Healthcare and humanitarian systems
  • Renewable energy generation
  • Process systems planning
  • Logistics management

decision_making
Decision making under uncertainty

  • Endogenous uncertainty
  • Robust optimisation
  • Stochastic programming
  • Time series aggregation

decomposition
Efficient formulations and solution methods

  • Convexification techniques
  • Cutting planes & column generation
  • Lagrangian-based decomposition methods
  • Parallelisation

People

Research Group

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Fabricio Oliveira

Associate Professor of Operational Research

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Juho Andelmin

Doctoral Candidate

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Lucas Condeixa

Doctoral Candidate

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Nikita Belyak

Doctoral Candidate

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Olli Herrala

Doctoral Candidate

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Helmi Hankimaa

Research Assistant

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Jaan Tollander de Balsch

Research Assistant

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Julius Beranek

Research Assistant

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Paula Weller

Research Assistant

Collaborators

Past Members

Projects

Blood units inventory management

Defining optimal inventory control policies using stochastic programming.

Decision Programming

Solving multi-stage decision problems under uncertainty, using influence diagrams and mixed-integer linear programming.

p-Lagrangian relaxation

Efficient decomposition methods for MIQCQPs using Lagrangian decomposition and parallelisation

Robust process systems

Developing models and methods for robust planning of process systems

Future energy systems modelling

Generation and Transmission Expansion Planning (GTEP) stochastic models incorporating temporal reduction via clustering algorithms.

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