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Elina Rönnberg

Deputy Head of Department, Senior Associate Professor

Discrete optimisation as decision support

Careful planning is essential to make efficient use of resources. For large-scale and complex systems, the use of mathematical optimisation can have a great impact on resource efficiency. Planning problems of this kind occur in many different sectors and the available resources can be anything from electronic components, vehicles, or machines to people that perform some tasks.

In situations when it is impossible for a human planner to fully grasp all the possibilities and choose a best possible plan, optimisation can be used to aid the decision process. This includes to formulate a mathematical model of the problem and to develop or select a solution method to compute a good, or preferably optimal, solution. Decision problems that are formulated to plan or schedule the use of resources often take the form of discrete optimisation problems.

Current research activities are described under the research domain Mathematics and algorithms for intelligent decision-making that introduces the work done in the group I’m leading.

Man som tittar på sin dator.

Discrete optimisation

My research area is discrete optimisation, with a special interest in decomposition methods and applications within scheduling and resource allocation. Our applied projects are often carried out in collaboration with industry or with other stakeholders. Examples of studied applications are the design of electronic systems in aircraft, staff scheduling in healthcare, underground mining, and railway crew planning. Some of these are highlighted in the list of research projects below.

Our research projects contribute to pushing the limits for when optimisation can be of practical use, both with respect to how a practically relevant problem is addressed and modelled, and through the development of efficient solution strategies.

Method development

On the method development side, areas of contribution include Dantzig-Wolfe decomposition, Lagrangian relaxation, column generation, branch-and-price, and logic-based Benders decomposition to hybridize MIP and CP. Other methodological contributions are within dynamic programming, decision diagrams for optimisation, metaheuristics, and mathheuristics.

Professional activities

Professional activities

  • WASP (Wallenberg AI, autonomous systems and software program) Research Management group in AI/Math
  • Specialist in Optimisation at Saab Aeronautics, 2014 - 2020
  • Co-founder of Schemagi, 2009 -

Student theses

  • Implementing an RCESPP solver for the Electric Vehicle Routing (Sub)Problem, Jenny Enerbäck, 2024. In collaboration with Scania.

  • A matheuristic method for a multi-vehicle search and rescue problem, Didrik Axén, 2024. In collaboration with Saab.

  • An optimisation approach to scheduling and planning of charging for heavy electric vehicles, Lukas Schildt, 2024. In collaboration with Scania.

  • List of student thesis in DIVA
  • List of student theses not in DiVA (PDF)

Current teaching

  • Introduction to Optimization (TAOP07) for Master of Science in Engineering programmes in Applied Physics and Electrical Engineering, Engineering Mathematics, Biomedical Engineering, Computer Science and Software Engineering, and for the Bachelor's programme in Mathematics.
  • Project - Applied Mathematics (TATA62) for Mathematics, Master's Programme, and Applied Physics and Electrical Engineering (Y and Yi)

Research domain

Research projects

Airplanes.

Resource allocation and scheduling for future avionic systems

To fully utilize the potential in modern modular avionic systems, very difficult allocation and scheduling problems may have to be solved. In cooperation with Saab Aeronautics, we develop specialized solution methods for future sysems.

Electrical truck.

Route planning of heavy-duty electric vehicles

Electrification of heavy-duty vehicles calls for intelligent methods to optimize the planning of their use and charging. With Scania and Ragn-Sells we develop mathematical models and algorithms for the next generation of transportation systems.

A SJ-train driving through a beautiful landscape.

Decision support for railway crew planning

For our society to fully reap the benefits of sustainable train travel, careful resource planning is essential. Passengers need to be able to rely on that trains are on time and train operators need to make efficient use of their vehicles and crew.

PhD students

Former PhD students

Publications

2023

Johannes Varga, Emil Karlsson, Günther R. Raidl, Elina Rönnberg, Fredrik Lindsten, Tobias Rodemann (2023) Speeding Up Logic-Based Benders Decomposition by Strengthening Cuts with Graph Neural Networks Machine Learning, Optimization, and Data Science, p. 24-38 Continue to DOI
Johannes Varga, Günther R. Raidl, Elina Rönnberg, Tobias Rodemann (2023) Interactive Job Scheduling with Partially Known Personnel Availabilities Optimization and Learning Continue to DOI
Stephen J. Maher, Elina Rönnberg (2023) Integer programming column generation: accelerating branch-and-price using a novel pricing scheme for finding high-quality solutions in set covering, packing, and partitioning problems Mathematical Programming Computation, Vol. 15, p. 509-548 Continue to DOI
Aigerim Saken, Emil Karlsson, Stephen J. Maher, Elina Rönnberg (2023) Computational Evaluation of Cut-Strengthening Techniques in Logic-Based Benders' Decomposition Springer Nature Operations Research Forum, Vol. 4, Article 62 Continue to DOI

2022

Emil Karlsson, Elina Rönnberg (2022) Instance dataset for a multiprocessor scheduling problem with multiple time windows and time lags: Similar instances with large differences in difficulty Data in Brief, Vol. 45, Article 108687 Continue to DOI
Emil Lindh, Kim Olsson, Elina Rönnberg (2022) Scheduling of an underground mine by combining logic-based Benders decomposition and a priority-based heuristic Proceedings of the 13th International Conference on the Practice and Theory of Automated Timetabling - PATAT 2022
Emil Karlsson, Elina Rönnberg (2022) Logic-based Benders decomposition with a partial assignment acceleration technique for avionics scheduling Computers & Operations Research, Instance dataset for a multiprocessor scheduling problem withmultiple time windows and time lags: Similar instances with largedifferences in difficulty, Vol. 146, Article 105916 Continue to DOI
Fabio F. Oberweger, Günther R. Raidl, Elina Rönnberg, Marc Huber (2022) A Learning Large Neighborhood Search for the Staff Rerostering Problem Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2022, p. 300-317 Continue to DOI

2021

Emil Karlsson, Elina Rönnberg (2021) Strengthening of feasibility cuts in logic-based Benders decomposition INTEGRATION OF CONSTRAINT PROGRAMMING, ARTIFICIAL INTELLIGENCE, AND OPERATIONS RESEARCH, p. 45-61 Continue to DOI
Matthias Horn, Johannes Maschler, Günther R. Raidl, Elina Rönnberg (2021) A*-based construction of decision diagrams for a prize-collecting scheduling problem Computers & Operations Research, Vol. 126, Article 105125 Continue to DOI
Emil Karlsson, Elina Rönnberg, Andreas Stenberg, Hannes Uppman (2021) A matheuristic approach to large-scale avionic scheduling Annals of Operations Research, Vol. 302, p. 425-459 Continue to DOI
Matthias Horn, Guenther R. Raidl, Elina Rönnberg (2021) A* Search for Prize-Collecting Job Sequencing with One Common and Multiple Secondary Resources Annals of Operations Research, Vol. 302, p. 477-505 Continue to DOI

Organisation