Fuzzy-based life-cycle cost model for decision making under subjectivity

Decision support models are needed to facilitate long-term planning and priority setting among competing alternatives. Life-cycle cost is the most frequently used economic model that considers all cost elements and related factors throughout the service life of the alternatives being considered. These cost elements and related factors are usually associated with uncertainty and subjectivity. As such, it is important to model the uncertainty arising from the assumed data over the service life of competing alternatives. Probabilistic techniques, such as Monte Carlo simulation, are commonly used to deal with such uncertainty or vagueness. However, they have been criticized for their complexity and amount of data required. This paper presents a fuzzy-based life-cycle cost model that accounts for uncertainty in a manner that disadvantages commonly encountered in probabilistic models are alleviated. The developed model utilized fuzzy set theory and interval mathematics to model vague, imprecise, qualitative, linguistic, and/or incomplete data. The model incorporates the equivalent annual cost method along with the Day-Stout-Warren (DSW) algorithm and the vertex method to evaluate competing alternatives. An example application is presented in order to demonstrate the use of the developed model and to illustrate its essential features.