Author
Ekinci, Y., Lu, J.-C., Duman, Ekrem
Publication Date
2015-05-01
Publication Place
-
Elsevier
Subject
Aggregation, Information based optimization, Model quality improvement, Logistics scheduling
Type
Periodical
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
1873-6793
Record ID
8056513a-ca00-4de3-bc63-d9d978eee05e
Library Location
Industrial Engineering
Date
2015-05-01
Notes
Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Sample Text
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
