Efficient Task Allocation and Path Planning for Unmanned Surface Vehicle in Supply Chain

The rapid development of autonomous transportation systems is currently motivating several research initiatives. This paper presents an approach for efficient task allocation and path planning for an Unmanned Surface Vehicle (USV) which is requested to transfer products in an island group in the Aegean Sea. The proposed approach is based on a bi-level scheduling methodology, in the upper level, considering the weather conditions and the geographical characteristics of the area we create a square (from/to) travel time matrix. In the lower level, considering travel times we develop a Genetic Algorithm to achieve the USV's task allocation and path planning. Experimental results demonstrate the effectiveness of our approach in guiding the USV's to efficiently accomplish short sea container transshipment in the island group of Cyclades.