Decision context based evaluation of multiattribute decision making methods

Multiattribute decision making (MADM) methods generally involve evaluating a set of decision alternatives by considering a set of evaluation criteria or attributes in order to achieve a decision outcome such as ranking and selection. The diversity among the decision problems in terms of their problem structures, characteristics, decision information and specific requirements has led to the development of numerous MADM methods. With the availability of many MADM methods, selecting the most suitable one for a given problem is a challenging task for the decision maker. The decision maker may not have required experience and expertise to understand the suitability of a method for a given problem. In order to help the decision maker select a suitable method, several guidelines have been developed along with empirical and simulation studies during the past few decades. Although existing studies provide valuable insights for selecting a suitable MADM method for specific decision problems, they are inadequate and unable to resolve several open research issues in MADM research, including: (a) unavailability of general guidelines for specific decision settings, (b) lack of method comparison experiments in detailed levels, (c) inability to find the most preferred method for specific decision contexts, (d) lack of objective measures to compare a set of suitable methods for a given problem, (e) inability to consider all the stakeholders in method evaluation, and (f) inadequacy of comparison studies for group decision methods. In this study, various decision contexts are identified to understand the decision settings and the decision maker’s evaluation and selection requirements. Six new methodologies are developed to resolve decision context specific issues in the area of MADM method evaluation, comparison and selection. A new simulation model is developed to provide decision setting specific method evaluation and selection guidelines. Experiments are conducted to illustrate applications of the new simulation model. This work highlights the need for detailed level method comparisons considering internal processes of MADM methods, including: normalisation procedures, aggregation techniques and consensus techniques. A new rank similarity based approach along with an objective measure is developed to compare a set of suitable methods for a given decision problem in order to find the most preferred one. The approach measures the similarity between ranking outcomes produced by the methods being evaluated. An alternatives-oriented approach is developed to provide due considerations to the decision alternatives in the method evaluation process, when they are key stakeholders. This approach provides a new dimension to method evaluation and selection. A comparison between the TOPSIS and the modified TOPSIS methods is conducted to justify the applications of these methods in MADM problem solving. Simulation experiments and mathematical proofs are provided to help the decision maker choose between them rationally. A new group consensus technique is developed to provide a much needed rational alternative to the existing techniques and to justify their usage. A novel consensus technique selection approach is developed to compare and evaluate group consensus techniques in an objective manner in order to find the one that most satisfies the group of decision makers as a whole. A new group decision method is developed based on comparative searching into the complete solution space that consists of all the possible decision outcomes. The method finds the solution preferred most by the whole group of decision makers. The research study contributes to the MADM research by introducing the concept of decision context based evaluation of MADM methods and developing new approaches, models and techniques to address context specific requirements in varying decision settings. This study also highlights the need for new perspectives towards the method evaluation processes. The research outcomes of this study have a great potential for practical problem solving. Various experimental results can be used as insightful guidelines for selecting the most suitable method for a given problem. With their simplicity and flexibility in concept and computation, the new approaches developed can be easily adopted to address new requirements in MADM method evaluation.