Optimization of ATM cash replenishment with group-demand forecasts
Küçük Resim Yok
Tarih
2015
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Pergamon-Elsevier Science Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In ATM cash replenishment banks want to use less resources (e.g., cash kept in ATMs, trucks for loading cash) for meeting fluctuated customer demands. Traditionally, forecasting procedures such as exponentially weighted moving average are applied to daily cash withdraws for individual ATMs. Then, the forecasted results are provided to optimization models for deciding the amount of cash and the trucking logistics schedules for replenishing cash to all ATMs. For some situations where individual ATM withdraws have so much variations (e.g., data collected from Istanbul ATMs) the traditional approaches do not work well. This article proposes grouping ATMs into nearby-location clusters and also optimizing the aggregates of daily cash withdraws (e.g., replenish every week instead of every day) in the forecasting process. Example studies show that this integrated forecasting and optimization procedure performs better for an objective in minimizing costs of replenishing cash, cash-interest charge and potential customer dissatisfaction. (C) 2014 Elsevier Ltd. All rights reserved.
Açıklama
Anahtar Kelimeler
Aggregation, Information Based Optimization, Model Quality İmprovement, Logistics Scheduling, Time-Series, Aggregation, Model
Kaynak
Expert Systems With Applications
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
42
Sayı
7
