The problem of highly constrained tasks in group decision support systems

Most experimental uses of group decision support systems (GDSS) are associated with relatively unrestricted domains, for example, idea generation and preference specification, where few restrictions on potential solutions exist. However, an important GDSS task is that of resource allocation across functional areas of the organization, including supply chain applications. These types of tasks, such as budget planning and production planning, are typically highly constrained and difficult to solve optimally, necessitating the use of decision aids, such as those found in GDSS. We use a model based on adaptive search of a genetic algorithm as the analogy for the group decision making process. We apply this model to experimental data gathered from GDSS groups solving a production planning task. The results indicate very low estimated crossover rates in the experimental data. We also run computational experiments based on adaptive search to mimic the GDSS data and find that the low estimated crossover rate might be due to the highly constrained search space explored by the decision making groups. The results suggest further investigation into the presumed beneficial effects of group interaction in such highly constrained task domains, as it appears very little true information exchange occurs between group members in such an environment. Furthermore, the simulation technique can be used to help predict certain GDSS behaviors, thus improving the entire GDSS process.