Bio-Inspired Optimization Method Supported Distributed Group Decision Making

Abstract

Distributed Group Decision Meeting (DGDM) is a multi-party decision problem where two or more independent concerned parties must make a joint decision. Group decision meetings consume a great deal of time and effort in organizations. To support these processes, Distributed Group Decision Support Systems (DGDSS) are used. They intended to provide computational support to collaborative decision-making processes. However, most of them are perceived to be extremely unproductive in terms of efficiently utilizing the participants’ time and effectively achieving the group decision meeting objectives. These shortcomings occur frequently because effective guidelines or procedures are not used. To overcome these problems, many DGDSS embed some facilitation mechanisms and are currently being used with the help of a human facilitator who guides the group members through the decision-making process. We consider in this article a framework for distributed group facilitation that supports facilitators by incorporating a model of the decision-making process which provides a detailed view of the decision-making process. Based on a model of the decision-making process, group facilitation tasks are automated, at least partially, to increase the ability of a facilitator to monitor and control the group decision meeting process. Decision support approaches such bio-inspired optimization methods potentially offer these capabilities and can assist the facilitator and decision-makers in presenting the alternatives in a form that facilitates the decision making. The developed system is based on Elephant herding optimization (EHO) whilst the evaluation is mainly based on Analytic Hierarchy Process (AHP).