A generic two-stage fuzzy inference system for dynamic prioritization of customers

This paper presents a fuzzy expert system for prioritization of heterogeneous customers with different needs. In this way, it is possible to improve the average waiting cost of a queuing system and enhance customer satisfaction as the basic component of organizations. Each customer has his own specific characteristics and demands and expects the system to understand his value and provide the best service. In this study, a two-stage Mamdani fuzzy inference system (FIS) was applied as a decision-making approach to prioritize customers based on Service Duration, Service Value, Customer Loyalty, Maximum Toleration, and Waiting Time. For this purpose, an expert system was developed that can be applied in various fields perfectly. To evaluate and analyze the performance of Fuzzy Prioritization System (FPS), the proposed model is compared with the First-In-First-Out (FIFO) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods as common approaches for prioritizing customers. The results of numerical experiments revealed the efficiency of the proposed system compared to these two methods.