Over the past decade, decision makers in the mining and mineral processing industry have had to deal with multiple challenges such as varying metal prices, depleting resource quality, and complying with stricter environmental regulations. Besides, the mining and mineral processing industry also needs to consider the use of renewable energy in order to comply with strict environmental regulations. In addition, since the use of renewable energy has costs associated to the additional equipment installation and maintenance, optimizing the equipment selection to minimise energy costs is of vital importance. Therefore, decision makers in the sector often face problems that involve multidisciplinary knowledge that take into account technical, social, economic and environmental aspects.

Multiple Criteria Decision Making (MCDM), which is a part of operations research, has become extremely useful to overcome a variety of decision making problems in mining and mineral processing. Although a very large number of MCDM methods have been developed, the effectiveness of these methods still depends on the objective of the decision making process (i.e. selection, sorting, ranking, description) and the nature of the problem.

An exhaustive literature review on the applications and trends of MCDM for the selection problem in mining and mineral processing has been conducted in this work. The literature review indicates that conventional MCDM methods have been frequently criticised on several drawbacks including its inability to quantify the uncertainty in data and information, the occurrence of the rank reversal phenomenon, and its difficulty in aggregating several judgements and preferences from multiple decision makers including related uncertainty on their judgements or preferences, as well as the robustness of MCDM methods in dealing with non-homogenous data (i.e. quantitative and qualitative).

This thesis presents the development of four new MCDM methods and their application to the selection problem (i.e. determining the best option from a set) in mining and mineral processing by taking into account the role of uncertainties in the decision making process. This work mainly demonstrates the value of applying the concept of constrained fuzzy arithmetic in fuzzy extension of conventional MCDM methods when the input data that need to be analysed are difficult to define precisely. In order to showcase the capability of the developed methods, three case studies on the selection problem in the mining industry were conducted. Furthermore, the robustness of the developed methods are shown by conducting sensitivity analyses and comparing their results to those obtained from existing methods.