Robustness analysis in truck-drone routing problems

Introduction​

In recent years, there has been a rise in the use of drones in various areas, including logistics. After a disaster, a particular logistical problem occurs when the infra-structures collapse and difficult-to-access areas arise. In these situations it is necessary to provide first healthcare aid with a combination of 1 drone and 1 truck.

Method​

The present work consists in the study of the influence of different real factors on the solutions obtained in a previous work. The aim of this work is to support the decision making on the route to be implemented, considering real factors not usually taken into account. Among the usual assumptions in these routing problems is high battery reliability.

We propose using a multirotor system that will be operated with battery swaps to overcome the endurance limitation. The drone will fly over a set of challenging locations for delivering healthcare aid. The number of locations that the drone is able to visit on each sortie is limited by battery capacity. The ground vehicle (GV) plays the role of recharging station, and it is assumes that it just moves along a straight line between two points. This research focuses in the optimization of the completion time.

Simulation studies have been carried out following a full factorial design of experiments followed by an ANOVA analysis. The design of experiments contains two factors. The first factor includes the different endurance levels of batteries. The second factor is the drone type.

Results​

The results consist of a study of the influence of various factors on the robustness of the planned route. To this end, the importance level of the influences of both the different factors and their possible interactions has been evaluated.

Conclusion​

The results obtained are promising in terms of the importance of considering various real influence factors, and therefore assess the risk of successfully carrying out the planned mission, previously optimized with the corresponding optimization algorithm.

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