Incremental Search-Based Allocation of Autonomous Robots for Goods Delivery

摘要

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.