A bi-objective scheduling and sequencing model for the full maintenance of automated teller machines
Strategies of financial institutions on cash replenishment and maintenance for a network of automated teller machines should be treated as a whole instead of independent of one another. Cash replenishment is a type of maintenance, and having separate service providers conduct this type of maintenanc...
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Main Authors: | , , |
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Format: | text |
Language: | English |
Published: |
Animo Repository
2019
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Subjects: | |
Online Access: | https://animorepository.dlsu.edu.ph/etd_bachelors/6008 |
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Institution: | De La Salle University |
Language: | English |
Summary: | Strategies of financial institutions on cash replenishment and maintenance for a network of automated teller machines should be treated as a whole instead of independent of one another. Cash replenishment is a type of maintenance, and having separate service providers conduct this type of maintenance adds costs and liabilities to the financial institutions. The optimal strategy to be employed focuses on the reduction of cash-related expenses, while maintaining the appropriate banknote-levels in each ATM. This minimizes cash-dry instances on individual teller machines. With the increasing concern of financial institutions for customer satisfaction, the optimal strategy will also consider the amount of maintenance to be conducted for each ATM minimizing downtime on individual teller machines. The ATM cash replenishment and maintenance, or full maintenance problem is treated as a scheduling problem specifically considering the need to conduct a proactive service instead of the industry practice of a reactive service. Factors such as daily service capacity, replenishment need, maintenance need, available time, travel time and service time are to be considered. The proposed model is able to recommend schedules for financial institutions to follow during instances where multiple machines are expected to experience a breakdown on a certain day, as well as single replenishments spread throughout different days. Furthermore, a scenario analysis will be conducted to show the different impacts on the factors, allowing financial institutions to have such considerations when constructing their proactive service scheduling. |
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