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There is a growing interest in using electric vehicles (EVs) and drones formany applications. However, battery-oriented issues, including range anxietyand battery degradation, impede adoption. Battery swap stations are onealternative to reduce these concerns that allow the swap of depleted for fullbatteries in minutes. We consider the problem of deriving actions at a batteryswap station when explicitly considering the uncertain arrival of swap demand,battery degradation, and replacement. We model the operations at a battery swapstation using a finite horizon Markov Decision Process model for the stochasticscheduling, allocation, and inventory replenishment problem (SAIRP), whichdetermines when and how many batteries are charged, discharged, and replacedover time. We present theoretical proofs for the monotonicity of the valuefunction and monotone structure of an optimal policy for special SAIRP cases.Due to the curses of dimensionality, we develop a new monotone approximatedynamic programming (ADP) method, which intelligently initializes a valuefunction approximation using regression. In computational tests, we demonstratethe superior performance of the new regression-based monotone ADP method ascompared to exact methods and other monotone ADP methods. Further, with thetests, we deduce policy insights for drone swap stations.

transportation science

A Monotone Approximate Dynamic Programming Approach for the Stochastic  Scheduling, Allocation, and Inventory Replenishment Problem: Applications to  Drone and Electric Vehicle Battery Swap Stations