Complex optimisation problems, which are concerned with optimising a given aspect of a complex system, such as time or energy, are difficult to solve. Often a range of solutions exist, and the difficulty lies in determining which solutions to implement in which part of the system. Within this work, a novel method is developed that allows the solver to overcome the key challenges for these types of problems, which are: defining the system parts (subsystems); minimising model complexity; quantifying solution effectiveness; and identifying relationships between solutions and subsystems. The method is demonstrated through application to the problem of railway traction energy saving. Subsystems are defined using quantified network and service characteristics. For each subsystem, the trends between six key solutions and the key performance indicators are analysed using multivariate data analysis and visualisation techniques. The relationships between subsystems are then explored at system level. The analysis determines the suitable solutions for each type of railway, providing information for operators about which solutions to target. Based on the results, the implementation of permanent magnet motor technology is considered, illustrating that the method is a suitable tool for informing further studies.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:720723 |
| Date | January 2017 |
| Creators | Steele, Heather Jane |
| Publisher | University of Birmingham |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://etheses.bham.ac.uk//id/eprint/7622/ |
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