Research of an optimization model for servicing a network of ATMs and information payment terminals
The steadily high demand for cash contributes to the expansion of the network of Bank payment terminals. To optimize the amount of cash in payment terminals, it is necessary to minimize the cost of servicing them and ensure that there are no excess funds in the network. The purpose of this work is to create a cash management system in the network of payment terminals. The article discusses the solution to the problem of determining the optimal amount of funds to be loaded into the terminals, and the effective frequency of collection, which allows to get additional income by investing the released funds. The paper presents the results of predicting daily cash withdrawals at ATMs using a triple exponential smoothing model, a recurrent neural network with long short-term memory, and a model of singular spectrum analysis. These forecasting models allowed us to obtain a sufficient level of correct forecasts with good accuracy and completeness. The results of forecasting cash withdrawals were used to build a discrete optimal control model, which was used to develop an optimal schedule for adding funds to the payment terminal. It is proved that the efficiency and reliability of the proposed model is higher than that of the classical Baumol-Tobin inventory management model: when tested on the time series of three ATMs, the discrete optimal control model did not allow exhaustion of funds and allowed to earn on average 30% more than the classical model.
Code (0)
등록된 구현이 없습니다.
Tasks
ManagementTime Series AnalysisSimilar Papers 제목 키워드 기반
The Policy Paradox: Government Debt Servicing and Local Bank Risk Growth
The issue of local government debt is widely recognized as one of the "gray rhinos" affecting the stable development of China's economy. Government debt can transmit risks to local banks, which are among the primary hold…
Optimal Action Extraction for Random Forests and Boosted Trees
Additive tree models (ATMs) are widely used for data mining and machine learning. Important examples of ATMs include random forest, adaboost (with decision trees as weak learners), and gradient boosted trees, and they ar…
Probabilistic Time Series Forecasts with Autoregressive Transformation Models
Probabilistic forecasting of time series is an important matter in many applications and research fields. In order to draw conclusions from a probabilistic forecast, we must ensure that the model class used to approximat…
Time SeriesTime Series AnalysisATMSeer: Increasing Transparency and Controllability in Automated Machine Learning
To relieve the pain of manually selecting machine learning algorithms and tuning hyperparameters, automated machine learning (AutoML) methods have been developed to automatically search for good models. Due to the huge m…
AutoMLBIG-bench Machine LearningSafe Multi-agent Satellite Servicing with Control Barrier Functions
The use of control barrier functions under uncertain pose information of multiple small servicing agents is analyzed for a satellite servicing application. The application consists of modular servicing agents deployed to…
ObjectPosition