A cost-aware QFD decision-making problem using guided firefly algorithm

Abstract

Satisfaction of customers is one of the ultimate goals of most companies and industries that may lead to increasing the amount of sales and earning revenue. Quality Function Deployment (QFD) as a well-known process for reaching this goal is applied in the literature. To apply QFD, it is necessary to solve QFD Decision-Making Problem (QFDDMP) in which using house of quality; engineers try to find the best solution among all possible solutions that satisfies customer requirements with minimal budget and time. In real problems, because of the abundant number of customers, customer requirements and constraints QFDDMP is known is an NP-hard optimization problem. Hence, it is required to apply efficient heuristic algorithms to solve the problem. In this study, by applying virtual attractiveness an improved version of Firefly Algorithm is proposed for solving QFDDMP. Virtual attractiveness is actually an attractiveness larger than the real amount to be given some fireflies to attract more fireflies and faster, to increase the speed of local search around them. Comparison of the obtained result to genetic algorithm, Particle Swarm Optimization and classic version of firefly algorithm it is proved that Guided Firefly Algorithm (GFA) could reach better solutions for QFDDMP with focus on minimizing the cost of the solutions. © Maxwell Scientific Organization, 2014.