*4.3. Expected Service Quality to Demands by the Zoning Approach*

The service quality for customers regarding UAV operations is another key factor that determines the success of a UAV-based service system. Given that immediate responses to demands determine service quality in many contexts, for emergency response in particular, we measure UAV response time to demands (i.e., the time interval between a service request receipt and a UAV arrival at the corresponding location). Figure 5 shows distributions of the mean response time over 20 simulations under the different problem classes and UAV deployment strategies.

(**b**) **Figure 5.** The mean response time to demands: (**a**) demand distribution U; (**b**) demand distribution C.

The results shows that the zoning approach outperforms the other considered service strategies in terms of the mean response time for customers. This is because the benchmark strategies allow UAVs to serve distant demands, whereas the zoning approach restricts such behavior by limiting the UAV operation area. Naturally, long-distance travel increases the individual UAV workload and the likelihood of being busy. Under the scenario FCFS, where there is a waiting queue, such a practice significantly increases the response time for customers (see the response time differences between the FCFS and NQ scenarios).

It should be noted that while the zoning approach can avoid such long-distance and inefficient travels, it is true that a demand generated when the responsible UAV is busy will not be served under the zoning approach, even if there could be other UAVs available nearby. When there is no waiting queue, such a practice might result in many demand nodes being abandoned.

To verify this issue, we compute the percentage of demands that left the system without receiving service, with regard to the total number of demands generated in a simulation run. Figure 6 shows the results for scenario NQ. As shown in the figure, this negative impact by the zoning approach seems marginal, and interestingly, the zoning approach even serves more demands than the other strategies. This is due to the efficient use of UAVs and corresponding low utilization levels of the units.

**Figure 6.** The percentage of demand loss under scenario NQ: (**a**) demand distribution <sup>U</sup>; (**b**) demand distribution C.
