Optimal delivery routing with wider drone-delivery areas along a shorter truck-route

Abstract

While convergent technology has been booming recently, drones are being applied in various fields of industry and are expected to be used as a commercial delivery method. In a delivery system, routing becomes one of the major issues, and several studies have attempted to solve drone-based routing problems. In this study, we focus on finding an effective delivery route for trucks carrying drones. To put it concretely, we propose a new approach on a nonlinear programming model to find shift-weights that move the centers of clusters to make for wider drone-delivery areas along shorter truck-route after initial K-means clustering and TSP (Traveling Salesman Problem) modeling. In order to verify the effectiveness of the proposed model with shift-weights, we compare it with two other delivery route approaches. One is a route without shift-weights after K-means clustering and TSP modeling, and the other is a route by TSP for all delivery locations without K-means clustering. Through experimental results of paired t-tests on randomly generated delivery locations, we show that our proposed model is more effective than the other two models.