Equipment Replacement Optimization

Abstract

TxDOT has a fleet value of approximately $500,000,000 with an annual turnover of about $50,000,000. Substantial cost savings with fleet management has been documented in the management science literature. For example, a 1983 Interfaces article discussed how Phillips Petroleum saved $90,000 annually by implementing an improved system for a fleet of 5300 vehicles. Scaling up to the TxDOT fleet, the corresponding savings would be around $350,000 in 2008 dollars. Similar savings were reported in a 2008 presentation by Mercury Associates. TxDOT Research Project 7-4941 (1997), Equipment Replacement Criteria Based on LCCBA, created a SAS decision analysis tool to be used by the department in its equipment replacement process. While the 7-4941 analysis tool met project scope within the data limitations existing at the time of its delivery, an improved vehicle cost data base will now allow a more normative decision support tool for fleet replacement optimization. In this sense, optimization means minimizing the life-cycle sum of maintenance cost and replacement cost (new equipment price minus resale value). The Department needs a system which recommends whether to retain or replace a unit of equipment, given that class of equipment's age, mileage, resale value, and the cost of replacement equipment. TxDOT categorizes, accounts for, and replaces equipment based on classes of equipment; the new automated fleet optimization system must use these class codes. The objective of this project is to (1) determine the best optimization methodology; (2) evaluate commercial fleet management systems; (3) develop the model if this is cost-effective relative to purchasing a commercial model; and (4) validate the new model as needed using data available on TxDOT's current fleet. To accomplish this project, the research team will formulate the equipment replacement optimization problem as a Mixed-Integer Linear Programming (MILP) model, and propose both Deterministic Dynamic Programming (DDP) and Stochastic Dynamic Programming (SDP) approaches to solving the Equipment Replacement Optimization (ERO) problem. Certainly, this system will be user-friendly and designed so that it can be easily used by non-technical district personnel (to evaluate individual district units against a class) and by technical division personnel (Fleet Manager) to develop optimal aggregate classcode replacement cycles.17.

Application of the Equipment Replacement Dynamic Programming Model in Conveyor Belt Replacement: Case Study of a Gold Mining Company

“…Therefore the Return of Investment (ROI) is maximised when replacement occurs at the optimal economic life of the equipment (Mellal et al, 2013;Fallahnezhad et al, 2014Gress et al, 2014. The economic life is defined as the age that minimises the average owning and operating costs (Mitchell, 1998;Fan et al, 2011;Rahimdel et al, 2013;Fallahnezhad et al, 2014). This includes the purchase, operating and maintenance cost, less any salvage value (du Plessis, 2007).…”