The freight sector is growing:​ disruptions in this sector have accelerated and are coming under more public scrutiny than other technological changes in the past. Autonomous goods delivery technologies are such a disruption, vehicles with no human on-board that can travel along roads, sidewalks, or in the air. Without the constraint of a human driver, many different forms and functions of vehicle can be created. It is uncertain the type or design of autonomous vehicle that will be best suited for exactly what scenarios, the strategies that will be used to operate them, and the supporting infrastructure changes that will be needed to best accommodate them. This thesis provides planning tools for this change that industry and government can utilize for better, quicker, and more transparent decisions. These methods also allow for sensitivity analysis and the impacts of technology changes to be considered and investigated. The impacts that uncrewed aerial vehicles (UAVs) and sidewalk autonomous delivery robots (SADRs) will have on the optimal locations of micro-fulfilment centers (MFCs) and mothership vans (MSs) are analyzed. It is found that the sensitivity of the relationship between stock-turnover by area and the optimal number of MFCs is a barrier to the adoption of UAV and MFC systems. Moreover, delivery time-windows are shown to be a primary motivating factor in the adoption of UAVs and the areas of a region that are most cost-effective for the switch to UAV and MFCs are the areas furthest from existing logistics centers. For SADRs working with MS, two distinct deployment strategies are identified and compared, then the breakeven cost points for these strategies are analytically determined and expressed in a decision matrix table. A series of sensitivity analysis scenarios shows how exurbs and further suburbs of urban areas are most cost-effective to move to MS with SADR systems. Finally, UAVs and SADRs are compared simultaneously, and a mixed fleet system considered and optimized with conventional vans. The numerical study of Singapore shows that increasing the autonomy of UAVs and SADRs is a key barrier for more widescale adoption.