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Research

At UBRI, we conduct research and funded projects on four domains:

Metaheurisic Optimization

Optimization means to choose between different alternatives and picking the best one. Many tasks in this very wide area are computationally, i.e., we cannot guarantee to find the best solution within acceptable runtime. Metaheuristic optimization algorithms try to find solutions which are as good as possible as fast as possible. At UBRI, we conduct research on optimization on practical optimization problems, on optimization algorithms (such as Evolutionary Algorithms), as well as on a meta-level, by finding new ways to experimentally analyze and compare optimization algorithms.

In the following, we present a selection of the research projects we are currently conducting on that domain.

  • TSPSuite, an automated benchmarking, evaluation, and comparison framework for algorithms for the Traveling Salesman Problem (TSP)
  • Large-Scale Optimization is the field of global optimization focusing on solving problems with hundreds, thousands, or even tens of thousands of decision variables

Logistics and Transportation

Logistics and transportation are among the most important services for any industry and society. They tackle the questions of how to best get objects and people from A to B. In China’s Logistics Industry Development Report 2013-2014, the total social logistics cost were given as 197.8 trillion RMB, accounting for 18 percent of the total GDP. In this domain, planning and optimization can lead to huge reductions in terms of costs, resource consumption, man power, and even pollution. For several years, researchers at UBRI significantly contribute to that field.

In the following, we present a selection of the research projects we are currently conducting on that domain.

  • Optimization for Logistic Tasks, i.e., solving hard tasks (such as TSPs, CARPs, and VRPTWs) from the domain of logistics
  • TSPSuite, an automated benchmarking, evaluation, and comparison framework for algorithms for the Traveling Salesman Problem (TSP)

Intelligent Underground Networks

In this project, we aim to use multi-sensor devices to detect underground pipelines and diagnose faults in these underground networks. In addition, we are designing intelligent sensors that can be attached to pipes, which can predict and help diagnose faults in the pipeline networks. This project will investigate hardware developments, signal processing, data fusion, data integration with geographic information systems (GIS), computer visions and etc.

Advanced Data Analytics

Our research on data analytics involves finding hidden or unknown relationships in data sets as well as advanced image analysis, e.g., for satellite imaging. It ranges from supervised to unsupervised Machine Learning as well as from small to Big Data.