Analyzing the Impact of Depreciation-Estimating Methods on State Transportation Agencies’ Equipment Replacement Decisions Using Dynamic Programming
Publication: Journal of Management in Engineering
Volume 39, Issue 4
Abstract
Replacing equipment at the most economical time not only helps to save state transportation agencies (STAs) costs for operating the fleet but also keeps the fleet’s level of service at an optimal level. Prior research studies focused on developing alternative economic-oriented equipment replacement models rather than the equivalent annual cost (EAC) model to achieve better economic decisions. In addition, various optimization techniques were applied to equipment replacement problems with different objectives, constraints, and contexts. However, few studies examined the impact of depreciation estimation on the equipment replacement decision within STAs by minimizing total equipment cost over a finite study period using the dynamic programming optimization method. This study performed a case study of two class codes of equipment [1.5 m3 (2-yd) diesel engine front-end loaders and 0.453 t (half-ton) fleetside pickup trucks] to analyze the impact of different depreciation calculations on equipment replacement decisions. Using real-world data provided by the Oklahoma Department of Transportation, the study showed that the double-declining balance depreciation method substantially reduces the number of pieces of equipment recommended for replacement compared with the result of the straight-line depreciation method. This study contributes to the understanding of the impact of depreciation methods on equipment replacement decisions as well as the importance of properly estimating equipment depreciation to minimize the equipment costs over a designated study period among STA communities. The demonstrated manual calculation procedures of dynamic programming for cost optimization potentially may facilitate STA’s adoption of dynamic programming for equipment economic decisions.
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Data Availability Statement
Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was supported by Louisianan State University and A&M College for the US Department of Transportation Consortium of South-Central States (Tran-SET).
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Published In
Journal of Management in Engineering
Volume 39 • Issue 4 • July 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Sep 5, 2022
Accepted: Feb 24, 2023
Published online: Apr 25, 2023
Published in print: Jul 1, 2023
Discussion open until: Sep 25, 2023
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