A Fuzzy Logic Based Optimal Network System for the Delivery of Medical Goods via Drones and Land Transport in Remote Areas

ABSTRACT

Land transport in rural areas are characterized by the abundance of road bends, varied road conditions, elevated terrains, and the far distance between each station. Such situations have rendered full-path drone delivery impossible, and thus deliveries using drones must inevitably be combined with land transportation in the whole process. Ever since the Covid-19 outbreak, there has been an unprecedentedly high demand for efficiency in the delivery of medical goods such as vaccines and medicines, especially in rural areas. Such measures prove indispensable in preventing the spread of the disease among all citizens. Conventionally, the abundance of road conditions, the length, and width of paths, and the characteristics of road bends, are considered and analyzed by human staff qualitatively using their experience and personal judgment before deciding on the best delivery path and the optimal network. To overcome the shortcomings of conventional methods, this article proposes a machine learning-based algorithm that considers all the different road conditions as well as the terrain elevations systematically and quantitatively to determine the best delivery path and construct the optimal delivery network system. When combined with drone delivery, our algorithm will also yield the most feasible position for the drone to be deployed and stationed to deliver the goods to the intended destinations, thereby creating a more comprehensive delivery network system. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.