An interactive group decision aid for multiobjective problems: An empirical assessment

Organizations are frequently required to make decisions about multiobjective problems. The complexity of such decision processes increases drastically when the participation of multiple decision makers becomes necessary. This is primarily due to the unique preference structures of the participants whose individual judgements of the 'best compromise solution' may not coincide. Nominal and/or interacting groups have been found to improve the decision-making effectiveness and efficiency associated with such multiple objective, multiple decision-maker problems. This study reports the results of a laboratory experiment involving the use of an interactive multiobjective group decision aid. The effect of two independent variables on a set of performance measures is investigated. The first independent variable is the presence or absence of a formal preference aggregation procedure in a group decision aid. The strength of decision-maker's linear programming background is the second independent variable. The dependent variables are solution quality, speed of convergence to a final agreement, and user confidence in the best compromise solution. Analysis and implications of the experimental results are provided and future research work is outlined.group decisions multiple criteria decision-making multiobjective programming interactive procedures empirical study

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Decision support systems are of many kinds depending on the models and techniques employed in them. Multiple criteria decision making techniques constitute an important class of DSS with unique software requirements. This paper stresses the importance of interactive MCDM methods since these facilitate learning through all stages of the decision making process. We first describe some features of Multiple Criteria Decision Support Systems ( MCDSSs) that distinguish them from classical DSSs. We then outline a software architecture for a MCDSS which has three basic components: a Dialog Manager, an MCDM Model Manager, and a Data Manager. We describe the interactions that occur between these three software components in an integrated MCDSS and outline a design for the Data Manager which is based on a concept of levels of data abstraction.Information Systems Working Papers Serie