Solution for the optimality of an intuitionistic fuzzy redundancy allocation problem for complex system using Yager’s ranking method of defuzzification with soft computation

Reliability optimization is of high concern in the field of system design, communication, industry etc. The reliability of a system can be maximized in several ways leading to different types of reliability optimization problems like, redundancy allocation problem (RAP), reliability redundancy allocation problem (RRAP). The RAP type of problems require suitable allocations to maximize the system reliability under certain restrictions. We have considered the RAP type problem in crisp environment and also the imprecise model of it is designed to explain the uncertainties of the model. In this paper, a real coded elitist genetic algorithm is employed for solving the redundancy allocation problem of a complicated system in intuitionistic fuzzy environment. All the control parameters of the system are considered as triangular intuitionistic fuzzy numbers. To crispify the different parameters of constrained intuitionistic fuzzy optimization problem, Yager’s ranking method of crispification is applied. The Big-M penalty technique is utilized to convert the constrained optimization problem into the unconstrained one. Finally, some numerical experiments are performed to maximize the system reliabilities for optimal component reliabilities. The sensitivity studies with respect to the GA parameters are presented graphically.