FADE: Towards Flexible and Adaptive Distance Estimation Considering Obstacles: Vision Paper

Abstract

In the last decades, especially intensified by the pandemic situation in which many people stay at home and order goods online, the need for efficient logistics systems has increased significantly. Hence, the performance of optimization techniques for logistic processes are becoming more and more important. These techniques often require estimates about distances to customers and facilities where operators have to choose between exact results or short computation times. In this vision paper, we propose an approach for Flexible and Adaptive Distance Estimation (FADE). The central idea is to abstract map knowledge into a less complex graph to trade off between computation time and result accuracy. We propose to further apply concepts from self-aware computing in order to support the dynamic adaptation to individual goals.