A generative solution for ATM cash management

Armenise, Roberto; Birtolo, Cosimo; Sangianantoni, Eugenio; Troiano, Luigi

doi:10.1109/socpar.2010.5686730

Cited by 10 publications

(11 citation statements)

References 10 publications

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“…Simutis, Dilijonas, Bastina, Friman, and Drobinov (2007) constructed an artificial neural network to forecast the daily cash demands for ATMs and estimate the optimal ATM cash loads-Venkatesh, Ravi, Prinzie, and Van Den Poel (2014) used neural networks too, but clustered ATMs preliminarily. Armenise, Birtolo, Sangianantoni, and Troiano (2010) used a specific genetic algorithm. Teddy and Ng (2011) forecasted the cashdemands with the help of cerebellar associative memory network, Ekinci, Lu, and Duman (2015) used group-demand forecasts.…”

Section: Relevant Literature On Cash Managementmentioning

confidence: 99%

Pareto improvement and joint cash management optimisation for banks and cash-in-transit firms

Ágoston

Benedek

Gilányi

2016

European Journal of Operational Research

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Available online xxx Keywords:OR in banking Cash-management Group decisions and negotiations Supply chain management a b s t r a c t Improving the ATM cash management techniques of banks has already received significant attention in the literature as a separate optimisation problem for banks and the independent firms that supply cash to automated teller machines. This article concentrates instead on a further possibility of cost reduction: optimising the cash management problem as one single problem. Doing so, contractual prices between banks and the cash in transit firms can be in general modified allowing for further cost reduction relative to individual optimisations. In order to show the pertinence of this procedure, we have determined possible Pareto-improvement re-contracting schemes based on a Baumol-type cash demand forecast for a Hungarian commercial bank resulting in substantial cost reduction.

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Section: Relevant Literature On Cash Managementmentioning

confidence: 99%

Pareto improvement and joint cash management optimisation for banks and cash-in-transit firms

Ágoston

Benedek

Gilányi

2016

European Journal of Operational Research

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“…In this case, the aim is to find the subset of K that when applied to a distinct ATM or to a group of them, minimizes the daily average ATM cash stock S within the time interval T that is [9]:…”

Section: Problem Statement and Solutionmentioning

confidence: 99%

A Methodology to Improve Cash Demand Forecasting for ATM Network

Darwish¹

2013

IJCEE

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Abstract-Developing cash demand forecasting model for ATM network is a challenging task as the chronological cash demand for every ATM fluctuates with time and often superimposed with non-stationary behavior of users. In order to improve the forecasting precision of ATM cash demand, an Interval Type-2 Fuzzy Neural Network (IT2FNN) has been utilized in this paper. The antecedent parts in each rule of the IT2FNN are interval type-2 fuzzy sets in view of conditions regarding time, location, cash residual and other factors that could lead to consider cash upload able to keep cash at the right levels to meet user demand. The employed IT2FNN has both on-line structure and parameter learning abilities. Simulation results for ATM cash forecasting show the feasibility and effectiveness of the proposed method.

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“…Then we used descriptive statistical procedures for result analysis. The proposed study uses a method originally developed by Armenise et al (2010) for GA implementation as well as simulation procedure.…”

Section: The Proposed Modelmentioning

confidence: 99%

“…Adendorff (2000) presented a scientifically-based decision-making procedure to determine the amount of cash to be held at a cash point of a retail bank at any time without compromising customer service levels or incurring undue expenditure. Armenise et al (2010) presented an application of genetic algorithms (GA) as metaheuristics for searching and generating optimal upload strategies, able at the same time to minimize the daily amount of stocked money and to assure cash dispensing service.…”

Section: Introductionmentioning

confidence: 99%

ATM cash management using genetic algorithm

Ghodrati¹,

Abyak²,

Sharifihosseini³

2013

10.5267/j.msl

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Automatic teller machine (ATM) is one of the most popular banking facilities to do daily financial transactions. People use ATM services to pay bills, transfer funds and withdraw cash. Therefore, we can treat ATM as a tradition inventory problem and use simulation technique to analysis the amount of cash required on different occasions such as regular days, holidays, etc. The proposed model of this paper uses genetic algorithm to determine the replenishment cash strategy for each ATM. The survey uses all transactions accomplished during the fiscal years of 2011-2012 on one of Iranian banks named Ayande. The study categorizes various ATM based on the average daily transactions into three groups of low, medium and high levels. The preliminary results of our survey indicate that it is possible to do setup different strategies to manage cash in various banks, optimally.

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A generative solution for ATM cash management

Cited by 10 publications

References 10 publications

Pareto improvement and joint cash management optimisation for banks and cash-in-transit firms

Pareto improvement and joint cash management optimisation for banks and cash-in-transit firms

A Methodology to Improve Cash Demand Forecasting for ATM Network

ATM cash management using genetic algorithm

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