Towards Better Coordination of Rescue Teams in Crisis Situations: A Promising ACO Algorithm

Abstract

Crisis management challenges decision support systems designers. One problem in the decision making is developing systems able to help the coordination of the different involved teams. Another challenge is to make the system work with a degraded communication infrastructure. Each workstation or embedded application must be designed assuming that potential decisions made by other workstations are treated as eventualities. We propose in this article a multi-agent model, based on an ant colony optimization algorithm, and designed to manage the inherent complexity in the deployment of resources used to solve a crisis. This model manages data uncertainty. Its global goal is to optimize in a stable way fitness functions, like saving lives. Moreover, thanks to a reflexive process, the model manages the effects of its decisions into the environment to take more appropriate decisions. Thanks to our transactional model, the system takes into account a large data amount and finds global optimums without exploring all potential solutions. Users will have to define a rule database using an adapted graphical interface. Then, if the nature of the crisis is deeply unchanged, users should be able to change the rule databases.