A Hybrid IVFF-AHP and Deep Reinforcement Learning Framework for an ATM Location and Routing Problem

A Hybrid IVFF-AHP and Deep Reinforcement Learning Framework for an ATM Location and Routing Problem

The impact of alternative distribution channels, such as bank Automated Teller Machines (ATMs), on the financial industry is growing due to technological advancements. Investing in ideal locations is critical for new ATM companies. Due to the many factors to be evaluated, this study addresses the pr...

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Bibliographic Details
Main Authors: Bahar Yalcin Kavus, Kübra Yazici Sahin, Alev Taskin, Tolga Kudret Karaca
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Applied Sciences
Subjects:
location selection
clustering
automatic teller machines
Fermatean fuzzy sets
routing optimization
deep reinforcement learning
Online Access:https://www.mdpi.com/2076-3417/15/12/6747
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Summary:The impact of alternative distribution channels, such as bank Automated Teller Machines (ATMs), on the financial industry is growing due to technological advancements. Investing in ideal locations is critical for new ATM companies. Due to the many factors to be evaluated, this study addresses the problem of determining the best location for ATMs to be deployed in Istanbul districts by utilizing the multi-criteria decision-making framework. Furthermore, the advantages of fuzzy logic are used to convert expert opinions into mathematical expressions and incorporate them into decision-making processes. For the first time in the literature, a model has been proposed for ATM location selection, integrating clustering and the interval-valued Fermatean fuzzy analytic hierarchy process (IVFF-AHP). With the proposed methodology, the districts of Istanbul are first clustered to find the risky ones. Then, the most suitable alternative location in this district is determined using IVFF-AHP. After deciding the ATM locations with IVFF-AHP, in the last step, a Double Deep Q-Network Reinforcement Learning model is used to optimize the Cash in Transit (CIT) vehicle route. The study results reveal that the proposed approach provides stable, efficient, and adaptive routing for real-world CIT operations.
ISSN:2076-3417

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