## 6 Computational Results and Discussion
### \(Co_{2}\) Results Comparison
Furthermore, it can been observed that along with higher demand, the percentage of customers picking up their parcel by foot or bike needs to be further increased (for all APL configurations) to achieve better results than for direct deliveries. With respect to the original assumption of 50% _by foot or bike_, for a vehicle capacity of 250 parcels (serving the APLs) only the APL configuration A6 is below or within a  
Figure 16: CO2 emissions by APLs and private vehicle per day for the different options with C = 500. Figure 15: CO2 emissions by APLs and private vehicle per day for the different options with C = 250.  
closer range, with 36% for demand scenario H and up to 61% for scenario A6 (Figure 17a). Increasing the vehicle capacity to 500 parcels leads to a different picture. Here, configuration A6 would lead to better results than DD, even if only a very small percentage (at most, 9% of the customers for scenario (S3) would walk or take a bike to pick up their parcel, with a distance between 0.3 and 1.5 km (Figure 17b)). In this case, APL configuration A5 also leads to comparable results with a percentage of customers, with a distance between 0.3 and 1.5 km to the nearest APL that pick up their parcels by foot or bike ranging between 47% (H) and 56% (S3) as a break-even point. When introducing less than 250 APL locations (A1-A4), better results can only be obtained if the percentage is much higher than the assumed 50%. Similar trends can be observed when increasing the ratio of customers choosing public transport instead of a private vehicle (Figure 18). Here, the impact is higher than for the first parameter observed, as for all APL system configurations a parameter setting can be found that leads to superior results for all APL scenarios, with the exception of A1. For ALP configuration A1 and demand scenario S3, however, 95% of customers (not already walking or taking a bike to reach the APL location) would have to choose public transport instead of a private car, which is not a realistic assumption. As currently only about 28% of the citizens of Dortmund choose public transport instead of private vehicles, again only scenario A6 is within a realistic range for a vehicle capacity of 250 parcels (Figure 18a), with a parameter range between only 6% (H) and 40% (S3). For a vehicle capacity of 500 parcels, APL configuration A5 also leads to comparable results, with the break-even point ranging from 20% (H) to 35% (S3). Figure 17: Sensitivity analysis for parameter ratio by foot or bike (distance between customer and APL between 0.3 and 1.5 km). Figure 18: Sensitivity analysis for parameter ratio public transport. In Figure 19, the results for the sensitivity analysis for the parameter _ratio of APL as a stop on an individual tour_ are given. Here, the respective break-even points are relatively high for most scenarios. For APL configuration A1, no comparable results can be obtained, even if 100% of all customers picking up parcels at the APL location by car do this as part of an individual tour (for both capacity configurations of the vehicles serving the APLs from the central depot). For demand scenario S3, superior results for the APL systems can be found for APL configuration A6 for a vehicle capacity of 500 parcels. For a vehicle capacity of 250, for demand scenarios S2 and S3 the percentage has to be higher than the originally assumed 50% to obtain better results for the APL configuration A6. For APL configuration A5, comparable results can be obtained for a vehicle capacity of 500 parcels, with values ranging from 45% (H) to 59% (S3). For all other APL configurations with 150 APL locations or less, the percentage of customers picking up parcels as part of an individual tour needs to be between 68% and 98% to reach the break-even point in comparison to direct deliveries (depending on the respective scenario).