On a beautiful day in May 2015, I drove the 13 hours from my home in Portland, Oregon, to Harris Ranch, California, halfway between San Francisco and Los Angeles. At the time, Tesla was touting a battery-swap station that could send Tesla drivers on their way in a fully powered vehicle in less than the time it takes to fill up a car with gas. Overtaken by curiosity, I had decided to spend a long Memorial Day weekend in California's Central Valley to see if Elon Musk's latest bit of dream weaving could stand up to reality. There, amid the pervasive stench of cow droppings from a nearby feedlot, I discovered that Tesla's battery swap station was not in fact being made available to owners who regularly drove between California's two largest cities. Instead, the company was running diesel generators to power additional Superchargers (the kind that take 30 to 60 minutes to recharge a battery) to handle the holiday rush, their exhaust mingling with the unmistakable smell of bullshit.

We consider the problem of optimizing the distribution operations of a hub using drones to deliver medical supplies to different geographic regions. Drones are an innovative method with many benefits including low-contact delivery thereby reducing the spread of pandemic and vaccine-preventable diseases. While we focus on medical supply delivery for this work, it is applicable to drone delivery for many other applications, including food, postal items, and e-commerce delivery. In this paper, our goal is to address drone delivery challenges by optimizing the distribution operations at a drone hub that dispatch drones to different geographic locations generating stochastic demands for medical supplies. By considering different geographic locations, we consider different classes of demand that require different flight ranges, which is directly related to the amount of charge held in a drone battery. We classify the stochastic demands based on their distance from the drone hub, use a Markov decision process to model the problem, and perform computational tests using realistic data representing a prominent drone delivery company. We solve the problem using a reinforcement learning method and show its high performance compared with the exact solution found using dynamic programming. Finally, we analyze the results and provide insights for managing the drone hub operations.

There is a growing interest in using electric vehicles (EVs) and drones for many applications. However, battery-oriented issues, including range anxiety and battery degradation, impede adoption. Battery swap stations are one alternative to reduce these concerns that allow the swap of depleted for full batteries in minutes. We consider the problem of deriving actions at a battery swap station when explicitly considering the uncertain arrival of swap demand, battery degradation, and replacement. We model the operations at a battery swap station using a finite horizon Markov Decision Process model for the stochastic scheduling, allocation, and inventory replenishment problem (SAIRP), which determines when and how many batteries are charged, discharged, and replaced over time. We present theoretical proofs for the monotonicity of the value function and monotone structure of an optimal policy for special SAIRP cases. Due to the curses of dimensionality, we develop a new monotone approximate dynamic programming (ADP) method, which intelligently initializes a value function approximation using regression. In computational tests, we demonstrate the superior performance of the new regression-based monotone ADP method as compared to exact methods and other monotone ADP methods. Further, with the tests, we deduce policy insights for drone swap stations.