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.

Autonomous robots can solve different issues of delivery services, by guaranteeing less traffic congestion, less pollution, and lower operational costs. Designing such type of delivery system based on autonomous robots requires the collaboration of different stakeholders, having different concerns: the store utilising the delivery service that is interested in costs and customer satisfaction, the municipality where the service is operated that is interested in the safety of the service, and the robotic company providing the service that is interested in all previous concerns. Our industrial partner from the robotic domain is designing this type of service in a smart town, and using a simulator for assessing different configurations providing different levels of performance. Since manually designing the configurations is time consuming for engineers, in this paper, we propose a search-based approach (All) that is able to explore the space of service configurations and find the optimal ones that show the tradeoff existing among the different concerns, so that stakeholders can make an informed decision. Since assessing one configuration requires to simulate the service multiple times over different types of customer requests, the approach suffers from scalability issues. Therefore, we propose two improvements of the approach that reduce the number of required simulations (IncrSim), and the duration of the simulation (IncrTime). Ex-periments on different settings show that IncrSim and IncrTime can find results as good as those of All in less time, and better than versions of All executed for the same budget.

Autonomous robots have recently been employed for goods delivery, with the goal of reducing traffic congestion, pollution, and operational costs. The design of such a delivery service requires to select the number of robots, their operating hours, and speed. Requirements from different stakeholders must be considered: customer satisfaction, cost, and safety. To assist with said design, our industry partner Panasonic is employing a search-based approach that tries to find service configurations that optimise the three requirements, on average, across different possible sets of customer requests. The obtained Pareto fronts of solutions show the trade-off existing among the different requirements. Such Pareto fronts, albeit very useful, do not always facilitate an informed decision for the stakeholders, for they provide too many solutions (some of them very similar to each other). To tackle this issue, in this paper we propose two approaches to prune and simplify Pareto fronts. Our approaches consider the standard deviation of objective values across the different sets of customer requests; the intuition is that, if two solutions (expressed in terms of average objective values) overlap based on their standard deviations, they can be considered similar. Based on this intuition, the two pruning approaches group similar solutions and select only one representative for each partition. We assessed these pruning methods on the Pareto fronts obtained with the search-based approach employed by Panasonic. We found that they can significantly reduce the size of the Pareto fronts while retaining a reasonable amount of their unpruned quality (measured in terms of Hypervolume).