By using consistency service quality enhancement techniques, service failure possibility is not entirely obliterated even so. In a competitive environment, minimizing time break between the service failure perception and the service recovery with the lowest cost is one of the fast responsiveness companies’ requirements. In this article, modeling service failure response time has been considered, and not only service recovery chain profit optimization was carefully planned but also satisfaction of the consumers who were disturbed by a service failure was considered profoundly. Inconsistency between the optimization of service recovery chain’s total benefit and the existing sections or firms’ local benefit was modeled by bi-level programming approach. The core recovery firm or department plays leader character, and in lower levels, there are firms or departments as followers that make local decisions in the service recovery chain. In this article, a heuristic algorithm was developed to solve the model, and by means of an applied case study, aspects of the results analysis were also riverweed.