Optimization of ATM cash replenishment with group-demand forecasts

Name: Optimization of ATM cash replenishment with group-demand forecasts
Author: Ekinci, Y., Lu, J.-C., Duman, Ekrem

İsim	Optimization of ATM cash replenishment with group-demand forecasts
Yazar	Ekinci, Y., Lu, J.-C., Duman, Ekrem
Basım Tarihi:	2015-05-01
Basım Yeri	- Elsevier
Konu	Aggregation, Information based optimization, Model quality improvement, Logistics scheduling
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1873-6793
Kayıt Numarası	8056513a-ca00-4de3-bc63-d9d978eee05e
Lokasyon	Industrial Engineering
Tarih	2015-05-01
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	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.
DOI	10.1016/j.eswa.2014.12.011
Cilt	42

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Optimization of ATM cash replenishment with group-demand forecasts

Yazar Ekinci, Y., Lu, J.-C., Duman, Ekrem

Basım Tarihi 2015-05-01

Basım Yeri - Elsevier

Konu Aggregation, Information based optimization, Model quality improvement, Logistics scheduling

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1873-6793

Kayıt Numarası 8056513a-ca00-4de3-bc63-d9d978eee05e

Lokasyon Industrial Engineering

Tarih 2015-05-01

Notlar Due to copyright restrictions, the access to the full text of this article is only available via subscription.

Örnek Metin 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.

DOI 10.1016/j.eswa.2014.12.011

Cilt 42