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Drone-based last-mile delivery is an emerging technology that uses drones loaded onto a truck to deliver parcels to customers. In this paper, we introduce a fully automated system for drone-based last-mile delivery through incorporation of autonomous vehicles (AVs). A novel problem called the autonomous vehicle routing problem with drones (A-VRPD) is defined. A-VRPD is to select AVs from a pool of available AVs based on crowd sourcing, assign selected AVs to customer groups, and schedule routes for selected AVs to optimize the total operational cost. We formulate A-VRPD as a Mixed Integer Linear Program (MILP) and propose an optimization framework to solve the problem. A greedy algorithm is also developed to significantly improve the running time for large-scale delivery scenarios. Extensive simulations were conducted taking into account real-world operational costs for different types of AVs, traveled distances calculated considering the real-time traffic conditions using Google Map API, and varying load capacities of AVs. We evaluated the performance in comparison with two different state-of-the-art solutions: an algorithm designed to address the traditional vehicle routing problem with drones (VRP-D), which involves human-operated trucks working in tandem with drones to deliver parcels, and an algorithm for the two echelon vehicle routing problem (2E-VRP), wherein parcels are first transported to satellite locations and subsequently delivered from those satellites to the customers. The results indicate a substantial increase in profits for both the delivery company and vehicle owners compared with the state-of-the-art algorithms. 

Food desert communities in the US have a widely recognized gap between the demand for healthy foods and the minimum order size that makes it worthwhile for food purveyors to deliver to such neighborhoods, thereby creating delivery deficiencies. A diverse set of mobility constraints and activity-travel patterns exist for disadvantaged segments in these communities, especially the elderly, unemployed, and socially excluded. Appreciating this complexity, an effective solution would be to improve the food access of such communities by providing faster, inexpensive, and flexible online deliveries of healthy foods. However, little is currently known about the shopping travel pattern in food desert communities and the associated mobility inequalities. This paper fulfills this critical research gap and quantifies the differences in shopping travel behavior observed among consumers residing in food deserts and food oases using data collected from Portland and Nashville Metropolitan areas. The paper subsequently captures the perceived acceptance of autonomous delivery robots (ADRs) among these consumers to overcome their mobility inequalities. The results indicate that food desert residents aged between 18 and 25 years, African Americans and those earning more than $75,000 are more likely to engage in internet shopping than food oasis residents. Despite the perceived potential of ADRs to reduce the mobility inequalities in food deserts, acceptance levels for this emerging technology are found to be significantly less among food desert residents, especially among older generational cohorts and less qualified. This study will provide key takeaways to e-commerce companies to expand their delivery service through ADRs in underserved areas. 

Truck platooning and related autonomous vehicle coordination concepts have been proposed as sustainable ways to increase profits and improve service quality. Recently the concept of truck caravanning, a hybrid truck platooning with only one truck driver required per platoon, has been proposed in the literature. This paper describes the research effort in developing a model that can estimate the cost savings of truck caravanning. The motivation of the proposed model is to investigate if substantial monetary savings exist to justify the initial capital investment (both in equipment and infrastructure) required for the implementation of the truck caravanning concept. A linear programming model is developed and used to evaluate different size networks. Results from numerical experiments indicate that a caravan size of four trucks or greater is needed for significant cost savings to be achieved and that driver compensation is the most critical factor dictating profitability.