Operations Research Methods for Scheduling and Resource Allocation Problems

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An inherent property of many decision problems of practical relevance is that they are computationally challenging. To solve them within a reasonable amount of time requires the development of specialised methods that exploit the mathematical structure of the problem.

Kvinna vid whiteboard med post it-lapparOperations research methods can be used to find solutions to decision problems of a quantitative nature. Many problems within scheduling and resource allocation, as for example planning of transports, timetabling and staff scheduling, can be formulated as discrete optimisation problems and solved by methods designed for such problems.

The scope of this research project is to develop mathematical models and solutions algorithms for discrete optimisation problems arising from scheduling and resource allocation applications.

Organisation

The project is funded by Center for Industrial Information Technology (CENIIT), and carried out at the Division of Optimization, Department of Mathematics, Linköping University. Project leader is Elina Rönnberg, PhD and Senior lecturer. The project involves the following sub-projects.

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Pilot framför instrumentpanelen i ett flygplan

Scheduling of Avionic Systems

In an avionic system, communications and activities need to be scheduled to ensure the functionality of the aeroplane. Efficient methods for such scheduling are of importance for the development of future avionic systems.
Insidan av en cockpit i ett flygplan

Operations Research Methods for Scheduling and Dimensioning of Real-Time Systems 

Decision problems for dynamic systems with timeliness or performance requirements can be formulated as discrete optimization problems.
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Decision Support for Scheduling: Models, Methods and Validation

In this project we have developed column generation methods for a kind of scheduling in telecommunication and for solving transportation problems with both fixed and linear costs.
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Mean-Variance Portfolio Optimization

We have developed solution methods for computationally challenging large scale instances of Markowitz's mean-variance portfolio optimization problem. We are also studying the cardinality-constrained version of the problem.

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