Abstract
System reliability optimization is often faced with imprecise and conflicting goals such as reducing the cost of the
system and improving the reliability of the system. The decision making process becomes fuzzy and multi-objective.
In this paper, we formulate the problem as a fuzzy multi-objective nonlinear program (FMOOP). A fuzzy multiobjective
genetic algorithm approach (FMGA) is proposed for solving the multi-objective decision problem in order
to handle the fuzzy goals and constraints. The approach is able flexible and adaptable, allowing for intermediate
solutions, leading to high quality solutions. Thus, the approach incorporates the preferences of the decision maker
concerning the cost and reliability goals through the use of fuzzy numbers. The utility of the approach is
demonstrated on benchmark problems in the literature. Computational results show that the FMGA approach is
promising.