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

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. Because of 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 compared with exact methods and other monotone ADP methods. Furthermore, with the tests, we deduce policy insights for drone swap stations.

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Informasi Detail

Judul Seri

TRANSPORTATION SCIENCE; Vol.56 No.4 July-August 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 1085-1110

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

MARKOV DECISION PROCESSES
APPROXIMATE DYNAMIC PROGRAMMING
ELECTRIC VEHICLES AND DRONES
BATTERY SWAP STATION
BATTERY DEGRADATION
MONOTONE POLICY AND VALUE FUNCTION
REGRESSION-BASED INITIALIZATION

Info Detail Spesifik

https://doi.org/10.1287/trsc.2021.1108

Pernyataan Tanggungjawab

Amin Asadi, Sarah Nurre Pinkley

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