Investigating Multi- and Many-Objective Search for Stability-Aware Configuration of an Autonomous Delivery System

Finding optimal configurations for complex systems, such as a fleets of autonomous delivery robots, is a complex task that benefits from automation. Automated search-based approaches have been proposed to automatically find such configurations. Although the configurations found by these methods perform well on average, they may be non-stable, i.e., their performance could vary greatly across scenarios. When deploying a system with a given configuration, it is important to know that it will perform adequately for the range of possible scenarios, i.e., to reduce how much the system's performance varies between scenarios. To this end, we attempt to make the search-based approaches aware of the configurations' stability. We explore two ways of doing this: by integrating it into the fitness functions describing the target performance metrics, and by adding it as a separate set of additional objectives. We applied the two approaches to find optimal configurations of a fleet of robots for automatic delivery service. Results show that integrating the stability concern into the fitness functions is better than treating it separately.