Constraint Programming Approach for Optimizing Business Asset Maintenance Strategy

Main Article Content

Gilang Ramadhan
Lecturer, Department of Business Administration, Politeknik Negeri Bandung, Indonesia.

Shu Shun Liu
Associate Professor, National Yunlin University of Science and Technology, Taiwan.

Keywords

Asset maintenance, Building maintenance, Constraint programming, Optimization

Abstract

There are many buildings with various conditions in Indonesia and some of them are not in finest conditions that need maintenance treatment urgently. The absence of building maintenance decision-making tool and limited budget are among main factors that cause unmanageable maintenance program. Therefore, this study has been conducted to propose an optimization model that is capable to determine the most appropriate building maintenance treatment. This study applied Constraint Programming (CP) approach to select the most economical maintenance treatment for a certain building and to allocate annual maintenance budget. CP-based model in this study subjects to constraint of budget and targeted level of building condition. In this study, maintenance treatment options, budget, time period, building deterioration rates, and the minimum standard of building condition were set. The model was run in IBM ILOG CPLEX Optimization Studio since the software is very efficient and effective in processing the optimization model. Furthermore, a case study was carried out to run the model involving 41 buildings in a 10-year period, and two different scenarios were conducted to examine the optimization model. The results successfully validated that the model can be a decision-making tool in selecting and prioritizing effective maintenance treatment.

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Jan 22, 2019

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How to Cite
Ramadhan, G., & Liu, S. (2019). Constraint Programming Approach for Optimizing Business Asset Maintenance Strategy. International Journal of Applied Business Research, 1(01), 70-87. Retrieved from https://ijabr.polban.ac.id/ijabr/article/view/42
Issue
Vol 1 No 01 (2019)
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