Scheduling Drones for Ship Emission Detection from Multiple Stations

Abstract

Various port cities and authorities have established emission control areas (ECAs) to constrain ships’ fuel usage in a specified offshore geographical range. However, these ECA policies involve high costs and have low monitoring and regulation enforcement efficiencies. In this study, a meeting model was used to investigate the drone-scheduling problem by considering the simultaneous movements of drones and ships. Set-covering integer linear programs were developed to formulate the assignments of drones to ships, and a model and solution algorithm were devised to determine the moving times and meeting positions for particular drones and ships. The proposed models and algorithms were employed and verified in experiments. The flying times for the datasets with three drone base stations were shorter than those with two. More drones resulted in shorter flying distances. The use of the meeting model enabled the acquirement of shorter flying times and distances than when it was not used. The datasets with more ships had longer flying times and distances, with almost linear relationships. The sensitivity of the effect of varying 5% of the ships’ speeds on the flying time metrics was less than 1%, affecting the flying distance by about 4–5%. Accelerating the drones was more effective towards optimizing the drones’ flying distances than times. Numerical studies showed that the consideration of simultaneous movements in the model allowed for a reduction in the drones’ flying distances and increased efficiency. Based on the modeling and experimental studies, managerial implications and possible extensions are discussed.