The steady growth of the e-commerce sector and the associated logisticalchallenges in the last mile, as well as the equally increasing expectations ofconsumers for parcel delivery call for innovation in the last mile. Drones androbots seem to be a reasonable alternative delivery option to meet thesechallenges. Before these technologies are used as means of transport in the lastmile, it is necessary to investigate whether it will be accepted by potentialconsumers. This thesis aims to identify the factors influencing conumser’ acceptance ofautonomous delivery vehicles for delivery in Germany. To determine thebehaviour of potential consumers, the Technology Acceptance Model wasextended by several factors from different acceptance models that seemedrelevant from a consumer perspective. In order to investigate consumer acceptance, a quantitative approach wasconducted using questionnaires. The propsed hypotheses were tested usingstructural equation modelling. Further, a multi-group analysis was conducted toindentify sociodemographic differences. The results show that price sensitivity, perceived usefulness, hedonic motivation,and perceived ease of use influence the behavioural intention of consumers inGermany to use autonomous delivery vehicles, whereas privacy security andfacilitating conditions do not have a significant effect. Further no significantdifferences were found in the multigroup analysis.

This dissertation integrates the strengths of individual emergent delivery technologies with package characteristics, and rural community needs to meet the demand for equitable, accessible, and inclusive rural delivery that is also cost-effective. To find ways to meet the package delivery service needs in rural areas and to fill research gaps in rural package delivery modeling, this study introduced a novel model known as the Parallel Scheduling Vehicle Routing Problem (PSVRP) in an endeavor to revolutionize package delivery by enhancing its efficiency, accessibility, and cost-effectiveness. The PSVRP represents a state-of-the-art approach to vehicle routing problems, incorporating a diversified fleet of innovative delivery modes. The multi-modal fleet of electric vans, ADVs, drones, and truck-drone systems works in unison to minimize operational costs in various settings. A solution methodology that implemented the Adaptive Large Neighborhood Search (ALNS) algorithm was designed to solve the PSVRP in this research to produce optimal or near-optimal solutions. A variety of scenarios in a rural setting that include different quantities of customers to deliver to and different package weights are tested to evaluate if a multi-modal fleet of electric vans, ADVs, drones, and truck-drone systems can provide cost-effective, low emissions, and efficient rural delivery services from local stores. Different fleet combinations are compared to demonstrate the best combined fleet for rural package delivery. It was found that implementation of electric vans, ADVs, drones, and truck-drone systems does decrease rural package delivery cost, but it does not yet decrease cost enough for the return on investment to be high enough for industry to implement the technology. Additionally, it was found that electric technologies do significantly decrease emissions of package delivery in rural areas. However, without a carbon tax or regulation mandating reduced carbon emissions, it is unlikely that the delivery industry will quickly embrace these new delivery modes. This dissertation not only advances academic understanding and practical applications in vehicle routing problems but also contributes to social equity by researching methods to improve delivery services in underserved rural communities. The PSVRP model could benefit transportation professionals considering technology-enabled rural delivery, developing rural delivery plans, looking for cost-effective rural delivery solutions, implementing a heterogeneous fleet to optimize rural delivery, or planning to reduce rural delivery emissions. It is anticipated that these innovations will spur further research and investment into rural delivery optimization, fostering a more inclusive and accessible package delivery service landscape. / Doctor of Philosophy / This dissertation integrates the strengths of individual emergent delivery technologies with package characteristics, and rural community needs to meet the demand for equitable, accessible, and inclusive rural delivery that is also cost-effective. To find ways to meet the package delivery service needs in rural areas and to fill research gaps in rural package delivery modeling, this study introduced a novel model known as the Parallel Scheduling Vehicle Routing Problem (PSVRP) in an endeavor to revolutionize package delivery by enhancing its efficiency, accessibility, and cost-effectiveness. The PSVRP represents a state-of-the-art approach to vehicle routing problems, incorporating a diversified fleet of innovative delivery modes. The multi-modal fleet of electric vans, ADVs, drones, and truck-drone systems works in unison to minimize operational costs in various settings. A solution methodology that implemented the Adaptive Large Neighborhood Search (ALNS) algorithm was designed to solve the PSVRP in this research to produce optimal or near-optimal solutions. A variety of scenarios in a rural setting that include different quantities of customers to deliver to and different package weights are tested to evaluate if a multi-modal fleet of electric vans, ADVs, drones, and truck-drone systems can provide cost-effective, low emissions, and efficient rural delivery services from local stores. Different fleet combinations are compared to demonstrate the best combined fleet for rural package delivery. It was found that implementation of electric vans, ADVs, drones, and truck-drone systems does decrease rural package delivery cost, but it does not yet decrease cost enough for the return on investment to be high enough for industry to implement the technology. Additionally, it was found that electric technologies do significantly decrease emissions of package delivery in rural areas. However, without a carbon tax or regulation mandating reduced carbon emissions, it is unlikely that the delivery industry will quickly embrace these new delivery modes. This dissertation not only advances academic understanding and practical applications in vehicle routing problems but also contributes to social equity by researching methods to improve delivery services in underserved rural communities. The PSVRP model could benefit transportation professionals considering technology-enabled rural delivery, developing rural delivery plans, looking for cost-effective rural delivery solutions, implementing a heterogeneous fleet to optimize rural delivery, or planning to reduce rural delivery emissions. It is anticipated that these innovations will spur further research and investment into rural delivery optimization, fostering a more inclusive and accessible package delivery service landscape.

The use of autonomous agents is an ever-growing possibility in our day-to-day life and, in some cases, already a reality. One future use might be autonomous robots performing last mile deliveries, a service the company HUGO delivery is currently developing. The goal of developing their autonomous delivery robot HUGO is to reduce the emissions from deliveries in the last mile by replacing delivery trucks with emission free autonomous robots. However, this new way of receiving deliveries introduces new design challenges since most people have little to no prior experience of interacting with autonomous agents. The user interface is therefore of great importance in making the user understand and be able to interact comfortably with the autonomous agent, thus also a key aspect in reaching user adoption. This thesis work examines how an interface for an autonomous food delivery service, such as the HUGO delivery, could be designed by applying a Human-Centered Artificial intelligence and Activity Centered Design focus in the design process, resulting in a design proposal for a web app. The conclusion of the thesis includes identification of the six essential interactions present in an autonomous food delivery service, as well as how HCAI and which of its guidelines can be applied when designing an interface for the interaction with an autonomous delivery robot.