Towards the Risk Assessment Framework Conceptualization Using Fuzzy Logic for Last-Mile Cargo Drones.

The use of autonomous and controlled drones for last-mile delivery of small cargo is rapidly advancing due to improvements in technology, artificial intelligence, and an increase in their cargo capacity. The application of drones to improve public services and meet transportation needs in today's world is an undeniable possibility. However, despite the clear benefits of using drones for last-mile delivery, there are potential risks, both known and new, that could lead to disruptions in the goods delivery. Hence, there is a necessity to design a method that will consider the impact of various risks on drone delivery sustainability at a significantly improved standard. Due to the diverse hazards nature associated with operating the aforementioned systems, it can be argued that to address these hazards and ensure sustainable delivery, it is essential to develop a flexible model using relevant approaches such as fuzzy logic or neural network modeling. Unlike probabilistic models, fuzzy logic does not necessitate extensive data. Additionally, an initial data sample on delivery risks posed by drones can be gathered through expert opinions. This research marks the initial phase in designing a fuzzy model for assessing risks in delivery systems using both autonomous and controlled drones by fuzzy logic. The study identified the key risks that primarily impact the drone deliveries' sustainability due to the expert survey. The risks were categorized into four groups: human factor; technical hardware issues; software failure; and weather conditions. Participants can help to identify the two most significant risk factors for each group. This information was used to determine the inputs for the logical-linguistic model. The fuzzy model defines five evaluation levels of supply sustainability. The key benefit is recommendations about the feasibility of drone use for last-mile delivery under specific conditions.

Keywords: Transportation, Hazard, Human factor, Technical hardware issues, Software failure, Weather conditions, Unmanned aerial vehicle, Autonomous vehicle, Controlled vehicle, Logico-linguistic model.