Optimal ATM Cash Replenishment Planning in a Smart City using Deep Q-Network

Kiyaei, Mohammadhossein; Kiaee, Farkhondeh

doi:10.1109/csicc52343.2021.9420561

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…The researchers approach ATM problems through different viewpoints, like location selection (Nazari-Ganje and Al-E, 2020), demand forecasting (Rafi et al , 2020) or cash replenishment (Chiussi et al , 2020; Kiyaei and Kiaee, 2021). The demand forecasting of these machines is one of the most interested part of the ATM-related literature and is also the focus of this study.…”

Section: Introductionmentioning

confidence: 99%

An averaging approach to individual time series employing econometric models: a case study on NN5 ATM transactions data

Cedolin

Genevois

2021

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PurposeThe research objective is to increase the computational efficiency of the automated teller machine (ATM) cash demand forecasting problem. It proposes a practical decision-making process that uses aggregated time series of a bank's ATM network. The purpose is to decrease ATM numbers that will be forecasted by individual models, by finding the machines’ cluster where the forecasting results of the aggregated series are appropriate to use.Design/methodology/approachA comparative statistical forecasting approach is proposed in order to reduce the calculation complexity of an ATM network by using the NN5 competition data set. Integrated autoregressive moving average (ARIMA) and its seasonal version SARIMA are fitted to each time series. Then, averaged time series are introduced to simplify the forecasting process carried out for each ATM. The ATMs that are forecastable with the averaged series are identified by calculating the forecasting accuracy change in each machine.FindingsThe proposed approach is evaluated by different error metrics and is compared to the literature findings. The results show that the ATMs that have tolerable accuracy loss may be considered as a cluster and can be forecasted with a single model based on the aggregated series.Research limitations/implicationsThe research is based on the public data set. Financial institutions do not prefer to share their ATM transactions data, therefore accessible data are limited.Practical implicationsThe proposed practical approach will be beneficial for financial institutions to use, that hold an excessive number of ATMs because it reduces the computational time and resources allocated for the forecasting process.Originality/valueThis study offers an effective simplified methodology to the challenging cash demand forecasting process by introducing an aggregated time series approach.

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Section: Introductionmentioning

confidence: 99%

An averaging approach to individual time series employing econometric models: a case study on NN5 ATM transactions data

Cedolin

Genevois

2021

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Multiobjective Logistics Optimization for Automated ATM Cash Replenishment Process

Thanh,

Van Tuan,

Chi

et al. 2023

Lecture Notes on Data Engineering and Communications Technologies

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Predicting Trends and Research Patterns of Smart Cities: A Semi-Automatic Review Using Latent Dirichlet Allocation (LDA)

Sharma¹,

Batra

Sharma³

et al. 2022

IEEE Access

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Smart cities are a current worldwide topic requiring much scientific investigation. This research instigates the necessity of an organized review to a heedful insight of the research trends and patterns prevailing in this domain. The string is formulated to extract the corpus from Scopus largest database of publications. The corpus of 8320 articles published from 2010 to 2022 is processed using Latent Dirichlet Allocation. Two, five, and ten topics have been extracted to provide the recent trends for IoT in smart cities. There has been an increased recognition that more attention needs to be paid to the area of smart cities so a complete overview of the topic of smart cities research, including the most prominent nations (institutions, sources, and authors) and noteworthy research directions has been presented in this paper. The scientific collaboration across countries (regions), organizations, and authors has also been widely discussed. A detailed and comprehensive overview and visualization of the trends and research patterns used to integrate the Internet of Things in Smart Cities. This data based experimental study signifies a roadmap of the research trends in Smart Cities by implementing topic modeling technique that has never been used in this domain. Based upon the topic modeling using LDA, authors have formulated three research questions and answered those question based on the in-depth research. At the end this study concludes the areas suggested are at the growing phase and need more insight for their growth.

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Optimal ATM Cash Replenishment Planning in a Smart City using Deep Q-Network

Cited by 3 publications

References 11 publications

An averaging approach to individual time series employing econometric models: a case study on NN5 ATM transactions data

An averaging approach to individual time series employing econometric models: a case study on NN5 ATM transactions data

Multiobjective Logistics Optimization for Automated ATM Cash Replenishment Process

Predicting Trends and Research Patterns of Smart Cities: A Semi-Automatic Review Using Latent Dirichlet Allocation (LDA)

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