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

  • Energy system planning
  • Healthcare and humanitarian logistics
  • Process scheduling/ routing

decision_making

Decision making under uncertainty

  • Decision programming
  • Stochastic programming
  • Robust optimisation

decomposition

Efficient formulations and solution methods

  • Lagrangian-based decomposition methods
  • Cutting planes-based methods
  • Convexification techniques
  • Column Generation

People

Research Group

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Fabricio Oliveira

Assistant Professor of Operational Research

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Hossein Mostafaei

Postdoctoral Researcher

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Olli Herrala

Doctoral Candidate

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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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Emilia Vuola

Research assistant

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Ilmari Vauhkonen

Research Assistant

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Jaan Tollander de Balsch

Research Assistant

Collaborators

Projects

Decision Programming

Framework for solving multi-stage decision problems under uncertainty, modeled using influence diagrams, and formulated using mixed-integer linear programming.

Inventory management

Defining optimal inventory control policies using stochastic programming.

p-Lagrangian

Efficient decomposition methods for MIQCQPs using Lagrangian decomposition and parallelisation

Robust process scheduling

Developing models and methods for robust planning of process systems

Energy Systems Modeling

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

Publications

(2020). Utility-scale energy storage in an imperfectly competitive power sector. Energy Economics.

PDF Project DOI

(2020). Combining penalty-based and Gauss-Seidel methods for solving stochastic mixed-integer problems. International Transactions in Operational Research.

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(2019). Decision Programming for Multi-Stage Optimization under Uncertainty.

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(2019). A robust optimization model for the maritime inventory routing problem. Flexible Services and Manufacturing Journal.

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