*5.2. Further Considerations for Zoning*

The followings can be further considered for the practical implementation and improved performance of the zoning approach. First, the connectivity between zones is important to guarantee relatively easy transition between zones. If the bases of zones were placed outside of the zones, the connectivity would be guaranteed by a common and shared area acting as a flight corridor for crossing UAVs.

Next, the shape of zones can be considered for potential flight trajectories within a zone. For smooth and efficient movement within a zone, convexity of the zone would be desired. This can be guaranteed by known methods, such as convex decomposition of an area [33]. When there is a clear temporal pattern in demand arrivals, dynamic zoning that changes a zoning solution over time can be considered. In this case, keeping the relevant adjustments (e.g., frequency of updated zoning solution) at a minimum should be pursued.

Finally, aligned with the proposed and implemented solution for a UTM that separates airspace by altitudes, a three-dimensional zoning approach can be designed. This approach can provide flexibility in forming a zoning solution and scalability for increasing UAV operation volumes.

#### **6. Concluding Remarks**

Considering that the zoning approach is initially proposed to guarantee the safety of UAVs during operations, the relevant service quality degradation for customers is expected due to the restricted movement of UAVs by the zoning approach. Unlike this concern, however, the zoning approach shows a dominant performance in our experiments compared to the other current UAV deployment strategies in terms of both the safety of UAVs and the service quality for customers. This is accordance with Sung and Nielsen [18]'s observation, where they examine the zoning approach with a single UAV station.

We also observed the trade-off between the performance criteria (safety vs. service quality) in the experiments, a natural phenomena of UAV-based service systems. To meet a desired service level of a UAV-based system while addressing this trade-off, further assistance from the tactical part of the UAV operation (e.g., UAV trajectory planning optimization) and a well-tuned zoning solution algorithm based on the system's priorities are essential for successful implementation of the zoning approach for a UAV-based service system.

Finally, let us highlight that the proposed zoning approach is a systemic solution for a UAV-based service system, which includes a tactical and operational solution for UAV deployment. The proposed approach does not require significant investments for a UTM, nor dramatic advances in UAV navigation and control technologies. Therefore, based on the demonstrated performance of the zoning approach and its implementation simplicity for a UAV-based service system, we believe that the zoning approach is a breakthrough for currently limited UAV applications and their deployment at large scale.

**Author Contributions:** Conceptualization, C.B.P., K.R., I.S. and P.N.; methodology, C.B.P., K.R., I.S. and P.N.; software, C.B.P. and K.R.; validation, C.B.P., K.R. and I.S.; writing—original draft preparation, C.B.P., K.R. and I.S.; writing—review and editing, I.S. and P.N.; visualization, C.B.P., K.R. and I.S.; supervision, I.S. and P.N. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: https://github.com/Rosenkrands/zav.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Appendix A. Genetic Algorithm Implementation**

We develop the GA such that it determines centroids of clusters from a predetermined set of candidate centroids in a service area. By doing so, a clustering solution can be derived while excluding the area where UAVs cannot fly (buildings, mountains, no-fly zones, etc.). With the area discretization, the zoning problem is to choose *K* number of centroids among a set of candidate centroids and to assign demand nodes to the selected centroids. To address this zoning problem with a discrete solution space, we implement the GA following the general purpose implementation presented by Scrucca [34].

A zoning solution is represented as a bit string for all candidate centroids, forming a chromosome of the GA. A value of one represents that a corresponding centroid is used for clustering; otherwise, it is zero. For the initial population we use 100 randomly generated chromosomes. The fitness for each chromosome is computed by the corresponding objective value. To generate the next population, the selection operator selects chromosomes from the current population following the probabilities, assigned to each chromosome of the population, inversely proportional to their fitness value. The selected chromosomes are further updated by applying the crossover operator with an 80% probability. The mutation operator that flips a bit of a chromosome is also applied to chromosomes with a probability of 10% in order to escape from a local optima.

#### **References**


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