In the construction industry, especially in infrastructure construction, the purchase of construction equipment is an important investment decision in terms of operational efficiency. In this process, many factors such as purchase cost, total working hour, model year, fuel consumption, service network, operation and maintenance cost, should be considered. Increasing the number of factors to be considered can make the decision-making process complex. In such cases, the use of decision support techniques will help decision makers to make the most accurate decision. The purpose of this study is to investigate the effectiveness of the AHP technique in the purchase of heavy construction equipment in the construction sector. In the evaluation of the alternatives determined by the purchasing unit, eight criteria were used, four quantitative and four qualitative. Data regarding quantitative criteria were obtained through market research conducted by the purchasing department, and data regarding qualitative criteria were obtained through face-to-face surveys with four experts. For the analysis, the Analytic Hierarchy Process (AHP) technique, which is widely used for decision support purposes, was used, and the criterion with the highest importance weight was determined as the periodic maintenance cost. The results obtained from the study show that multi-criteria decision making (MCDM) methods are effective to help decision makers in the purchase of heavy equipment in the construction industry.
Analytical Hierarchy Process (AHP) Heavy Construction Equipment Purchasing Multi-criteria Group Decision Making
The paper is carried out in accordance with ethical standards.
No specific funding for this study was received from commercial, public or non-profit organizations.
| Primary Language | English |
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| Subjects | Construction Business |
| Journal Section | Research Article |
| Authors | |
| Publication Date | December 1, 2025 |
| Submission Date | December 31, 2024 |
| Acceptance Date | September 5, 2025 |
| Published in Issue | Year 2025 Volume: 13 Issue: 4 |