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

APPLIED SCIENCES, cilt.15, sa.6747, ss.1-27, 2025 (SCI-Expanded, Scopus)

Yayın Türü: Makale / Tam Makale
Cilt numarası: 15 Sayı: 6747
Basım Tarihi: 2025
Doi Numarası: 10.2139/ssrn.5186710
Dergi Adı: APPLIED SCIENCES
Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Agricultural & Environmental Science Database, Applied Science & Technology Source, Communication Abstracts, INSPEC, Metadex, Directory of Open Access Journals, Civil Engineering Abstracts
Sayfa Sayıları: ss.1-27
Yıldız Teknik Üniversitesi Adresli: Evet

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.