Last mile delivery drone selection and evaluation using the interval-valued inferential fuzzy TOPSIS

Abstract

The last mile delivery option has become a focal point of academic research and industrial development in recent years. Multiple factors such as increased demands on delivery flexibility, customer requirements, delivery urgency, and many others are enforcing to adopt this option. For fulfilling this paradigm shift in delivery and providing additional flexibility, drones can be considered as a viable option to use for last mile delivery cases. Numerous drones are available in the market with varying capacities and functionalities, posing a significant challenge for decision-makers to select the most appropriate drone type for a specific application. In this purpose, this study proposes a comprehensive list of criterions that can be used to compare a set of available last mile delivery drones. Additionally, we introduced a systematic multi-criterion, multi-personnel decision making approach, referred to as interval-valued inferential fuzzy TOPSIS method. This method is robust and can handle the fuzziness in decision making, thereby providing quality drone selection decisions under different applications. We then apply this method to a real-life test setting. Results suggest that smaller drones or quadcopters are considered viable to use in urban environments while long-range drones are preferred for the last mile delivery needs in rural settings.