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

Afzal Siddiqui

Ahti Salo

Andrew Eberhard

Ellie Dillon

Lina Reichenberg

Silvio Hamacher

Tommi Ekholm

Posts

Hello World

This is the website of our research group $\Gamma$-opt (read Gamma-opt), which stands for Group of Applied Mathematical Modelling And OPTimisation. We were founded at some point in 2019, and we are part of the Systems Analysis Laboratory, with the department of Mathematics and Systems Analysis at Aalto University.
Aug 24, 2020 1 min read
Hello World

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.

Talks

Decision Programming: a framework For optimizing multi-stage decision problems under uncertainty

Multi-stage decision problems under uncertainty can be represented as influence diagrams consisting of decision, chance and value nodes …
Aug 8, 2019 7:25 PM Trondheim, Norway
Fabricio Oliveira, Juho Andelmin, Ahti Salo
PDF Project

The p-Lagrangian method for MIQCQPs

Mixed-integer quadratically constrained quadratic program- ming (MIQCQP) is a general class related to several important problems such …
Feb 24, 2019 7:25 PM Bordeaux, France
Fabricio Oliveira, Tiago Andrade, Silvio Hamacher
PDF

Revisiting inventory control using stochastic programming

Traditional methods for solving inventory control problems have their roots in tools such as queueing theory and dynamic programming. …
Feb 23, 2019 3:09 PM Valencia, Spain
Fabricio Oliveira
PDF

Optimization under Uncertainty in Real-World Problems: cases from the Oil & Gas Industry

Feb 23, 2019 2:47 PM RMIT University, Melbourne, Australia
Fabricio Oliveira
PDF

Tutorial on Robust Optimisation - modelling aspects and applications

Feb 23, 2019 2:46 PM Melbourne, Australia
Fabricio Oliveira
PDF
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Publications

Vilma Virasjoki, Afzal S. Siddiqui, Fabricio Oliveira, Ahti Salo (2020). Utility-scale energy storage in an imperfectly competitive power sector. Energy Economics.

PDF Project DOI

Olli Herrala (2020). An efficient strategy for solving stochastic programming problems under endogenous and exogenous uncertainties.

PDF Project

Fabricio Oliveira, Jeffrey Christiansen, Brian Dandurand, Andrew Eberhard (2020). Combining penalty-based and Gauss-Seidel methods for solving stochastic mixed-integer problems. International Transactions in Operational Research.

PDF DOI

Ahti Salo, Juho Andelmin, Fabricio Oliveira (2019). Decision Programming for Multi-Stage Optimization under Uncertainty.

PDF Project

Gustavo Souto dos Santos Diz, Silvio Hamacher, Fabricio Oliveira (2019). A robust optimization model for the maritime inventory routing problem. Flexible Services and Manufacturing Journal.

PDF DOI

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