Uncertainty Analysis Methods For Multi - Criteria Decision Analysis by Kylie Marie Hyde A

Page ii research, which may assist in negotiating sustainable decisions while fostering a collaborative learning process between DMs, experts and the community. The two uncertainty analysis approaches overcome the limitations of the existing sensitivity analysis methods by being applicable to multiple MCDA techniques, incorporating uncertainty in all of the input parameters simultaneously, identifying the most critical criteria to the ranking of the alternatives and enabling all actors preference values to be incorporated in the analysis. Five publications in refereed international journals have emerged from this research, which constitute the core of the thesis (i.e. PhD by Publication). The publications highlight how uncertainty in all of the input parameters can be adequately considered in the MCDA process using the proposed uncertainty analysis approaches. The methodologies presented in the publications are demonstrated using a range of case studies from the literature, which illustrate the additional information that is able to be provided to the DM by utilising these techniques. Publications 1 and 2 (Journal of Environmental Management and European Journal of Operational Research) demonstrate the benefits of the distance-based uncertainty analysis approach compared to the existing deterministic sensitivity analysis methods. In addition, the benefits of incorporating all of the input parameters in the uncertainty analysis, as opposed to only the CWs, are illustrated. The differences between global and non-global optimisation methods are also discussed. Publications 3 and 4 (Journal of Water Resources Planning and Management and Journal of Multi-Criteria Decision Analysis) present the stochastic uncertainty analysis approach and illustrate its use with two MCDA techniques (WSM and PROMETHEE). Publication 5 (Environmental Modelling & Software) introduces the software program developed as part of this research, which implements the uncertainty analysis approaches presented in the previous publications. Despite the benefits of the approaches presented in the publications, some limitations have been identified and are discussed in the thesis. Based on these limitations, it is recommended that the focus for further research be on developing the uncertainty analysis methods proposed (and in particular the program, and extension of the program) so that it includes additional MCDA techniques and optimisation methods. More work is also required to be undertaken on the Genetic Algorithm optimisation method in the distance-based uncertainty analysis approach, in order to simplify the specification of input parameters by decision analysts and DMs.