Skip to content
Research Article Open access CC BY 4.0

A Dynamic Programming Approach to Replacement of Transport Vehicles in Benin City, Nigeria

Ernest Oseghale Amiens, Michael D. Oisamoje, Anthony U. Inegbenebor

Journal of Advances in Mathematics and Computer Science · pp. 204–214 · Published 3 Jan 2015

10.9734/BJMCS/2015/15454

Abstract

Every organisation has an objective to optimise the utility function of its available operational assets. For commercial vehicle transport operators, the goal is to operate the vehicles for as long as they can make net contribution to the organisation’s corporate objective. Hence, when these vehicles are replaced becomes an issue for strategic decision making. Unfortunately many of the commercial bus transport companies lack the skill to undertake the required empirical evaluation necessary to provide objective data and information for making the vehicle replacement decisions. This study was therefore an effort to bridge this gap in knowledge. Only two out of the fourteen transporter companies of interest operating in Benin City, Edo State, Nigeria agreed to provide the required data for the study which covered the period 2008 to 2013 and for Toyota brand of buses only. The data was subjected to backward recursive dynamic programming analysis. The results showed that the four years fixed-age vehicle replacement policy employed by commercial bus transport companies in Benin City was optimum only for the Toyota high roof types of buses. The study thus recommends that commercial vehicle owners/operators should endeavour to keep reliable, relevant and up to records of their vehicles. While it is advocated that adequate and continuous training of key staff on equipment replacement should also be encouraged, operators of mass-transit systems can seek the assistance of Operations Research experts in order to enhance their decisions regarding vehicle replacement policies.

Backward recursive dynamic programming minimisation model toyota bus vehicle replacement replacement policy replacement strategy.

References (13)

  1. 1 Dynamic Programming [DOI]
  2. 2 Dynamic Programming: A Computational Tool
  3. 3 Equipment Replacement Policy [DOI]
  4. 4 A model for equipment replacement due to technological obsolescence [DOI]
  5. 5 An Introduction to Dynamic Programming [DOI]
  6. 6 Equipment Replacement in an Unsteady Economy [DOI]
  7. 7 Dynamic Programming Approach to Optimization of Site Remediation [DOI]
  8. 8 Development of Vehicle Replacement Programme for a Road Transport Company
  9. 9 Developing a framework for determining the optimal replacement strategy for a vehicle fleet of a 3rd Party logistics service provider (LSP), IMPERIAL Distribution
  10. 10 A mathematical approach to transportation related data of South Assam for the optimal age of replacement of a vehicle and daily expenditure minimization [DOI]
  11. 11 Network Generating Models for Equipment Replacement
  12. 12 Vehicle replacement in government parastatals: A comparative study of major government replacement policies [DOI]
  13. 13 Capital replacement modelling with a fixed planning horizon

Cited by 9

Optimisation of medical equipment replacement using stochastic dynamic programming

Waleed M Altalabi, M. Rushdi, B. Tawfik · Journal of Medical Engineering & Technology · 2020

Appraisal of Optimum Economic Life for Farm Tractor: A Case Study

V. Pagare · Economic Affairs · 2019

A model of vehicle replacement time with overloading cost constraint

H. Inegbedion, M. Aghedo · 2018

Replacement model of city bus: A dynamic programming approach

Dadang Arifin, Edhi Yusuf · 2017

Studies of tractor maintenance and replacement strategies of Wonji Shoa Factory, Ethiopia

Kishor P. Kolhe, Demelash G. Lemi, Siraj K. Busse · Journal of Agricultural Engineering · 2024

Showing 5 of 9 known citations — external sources report more than can currently be individually listed.

Related research

Article metrics

Real usage data collected on this platform.

4

Page views

0

PDF downloads

0

Outbound clicks

9

Citations

Views over time

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

Traffic sources

Referring site, by host.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.