IIARD International Institute of Academic Research and Development

WORLD JOURNAL OF INNOVATION AND MODERN TECHNOLOGY (WJIMT )

E-ISSN 2504-4766
P-ISSN 2682-5910
VOL. 9 NO. 4 2025
DOI: 10.56201/wjimt.v9.no4.2025.pg18.35

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Determining the Impact of the Maintenance and Salvage Cost’s Function Parameters on an Equipment Optimal Replacement Time Computation Under the Economic Life Cycle Strategy: Case Study of a Commuter Bus

Festus, Ow, Ralph O Edokpi

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Abstract

In the business of fleet management, operators or owners are faced with the difficult task of chosen between a fixed-time based replacement policy and a breakdown replacement strategy, in which a vehicle is replaced only when the repairs become nearly impossible. The focus in the choice made is whether it will attract a long- run cost effectiveness, as a fixed time scheduled if not properly modeled could result to underutilization of a vehicles, leading to profit loss or unnecessarily incurred high maintenance cost due to out of useful life cycle usage. In this study, a logarithmic transformation of costs data sets of an economic life cycle model is carried out to introduce the needed linearity in a vehicle age maintenance and salvage cost distribution for the determination of the respective costs function parameters required for a more accurate optimal replacement time estimation. The cost comparison of the improved model due to its data transformation and that of the untransformed data is done alongside their optimal replacement time results, to ascertain the sensitivity of the costs function parameters to the optimal replacement time. While the model based on transformed data reported a reasonable replacement time of 18 months replacement period in ten years planning horizon, that of the untransformed that reported an unrealistic optimal time that is intractable. Estimated costs and actual costs for the transformed data-based model showed little variations of about 0-150% for the maintenance cost which suffered greater disparity in the sourced data distribution and 0-109% for the salvage cycle costs. The model based on untransformed data set recorded wide variation in terms of 1000% increment in the estimated value over the actual recorded costs.

keywords:

Logarithmic: Linearity: Life – Cycle: Optimal Time: Salvage Costs: Operational Costs

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