Green and economic fleet replacement modeling : part I.

PDFTech ReportOTREC-RR-11-03OTREC 2010-305Equipment replacementFleet managementEnvironmental policyEnvironmental impactsOregonOregon. Dept. of Transportation. Research UnitKim, David S.Porter, J. DavidFigliozzi, Miguel A.Oregon State University. School of Mechanical, Industrial and Manufacturing EngineeringPortland State University. Dept. of Civil & Environmental EngineeringOregon. Dept. of Transportation. Research UnitOregon Transportation Research and Education ConsortiumUS Transportation Collection"The purpose of this study was to gain a better understanding of how equipment replacement decisions are supported with data collection andquantitative models at state DOTs, and to determine if models found in the research literature offer any better decision support when applied torealistic fleet usage and cost data. This study also addressed the current state of equipment replacement at state DOTs with respect to usingmeasurable \ue2\u20ac\u153green\ue2\u20ac? criteria in replacement decisions, and the development of new quantitative replacement models utilizing such criteria.The responses from 25 state DOTs indicates that there is little consistency in the criteria used by state DOTs to support replacement decisionsand the way that these criteria are used. There are also no measurable \ue2\u20ac\u153green\ue2\u20ac? criteria utilized. However, most state DOTs maintain aninformation system where cost and usage data is recorded, stored, and utilized as part of the replacement process. To investigate if a particularmodeling approach offers better performance than the variety of approaches used in practice, a simulation study was conducted. Simulationmodels were used to evaluate the effectiveness of replacement models for prioritizing equipment replacement. The models evaluated come fromthe research literature and were compared to a simple replacement model that is similar to those used by state DOTs. Results indicate that thesimple models used in practice provide similar results to the best model from the research literature.