Optimal fleet replacement: A case study on a Spanish urban transport fleet

• Published in: The Journal of the Operational Research Society Vol. 68; no. 8; pp. 886 - 894
• Main Authors: Riechi, J., Mácian, V., Tormos, B., Avila, C.
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis 01.08.2017; Taylor & Francis, Ltd; Palgrave Macmillan UK; Taylor & Francis Ltd
• Subjects: Asset management; Business and Management; Computer simulation; Cost analysis; Decision analysis; fleet replacement; Life cycle analysis; Life cycle assessment; Life cycle asset management; life cycle cost analysis; Life cycle costs; Life cycle engineering; Management; Monte Carlo simulation; operation and maintenance costs; Operations Research/Decision Theory; Optimization; transport; Urban transportation; Spain; transport; fleet replacement; optimization; life cycle cost analysis; Monte Carlo simulation; operation and maintenance costs
• ISSN: 0160-5682; 1476-9360
• DOI: 10.1057/s41274-017-0236-1

Optimizing the average annual cost of a bus fleet has become an increasing concern in transport companies management around the world. Nowadays, there are many tools available to assist managerial decisions, and one of the most used is the cost analysis of the life cycle of an asset, known as “life cycle cost”. Characterized by performing deterministic analysis of the situation, it allows the administration to evaluate the process of fleet replacement but is limited by not contemplating certain intrinsic variations related to vehicles and for disregarding variables related to exigencies of fleet use. The main purpose of this study is to develop a combined model of support to asset management based in the association of the life cycle cost tool and the mathematical model of Monte Carlo simulation, by performing a stochastic analysis considering both age and average annual mileage for optimum vehicle replacement. The utilized method was applied in a Spanish urban transport fleet, and the results indicate that the use of the stochastic model was more effective than the use of the deterministic model.