Abstract

We study a same -day delivery problem where customer orders arrive dynamically throughout the day and the service operator must determine, in real-time, whether to accept the orders and how to adjust the ongoing distribution plan. We develop a route -based Markov Decision Process and an efficient online policy to dynamically route a truck that can receive newly arrived orders along its route via drones dispatched from a depot. Numerical experiments show that our online policy has an average fill rate decrease of at most 20% over the perfect -information counterpart. Further, this online policy has a fill rate increase of up to 8% over a na & iuml;ve greedy policy. We also show that drone resupply increases fill rates by up to 21% compared to a conventional truck -only resupply system. Computational times to make each decision are in the hundredths of a second, thus allowing real-time feedback to customers regarding their eligibility for same -day delivery.