Customer Behavior Analysis With Risk Preference In The Stochastic Service System

Stochastic service system substantially exist in our real life, such as, buy the tickets in a queue, queueing in the park to enjoy the relative activities, queueing in the channel to procure the network spectrum, and so on, these are much close to our daily life. According to the development course, the study of the stochastic service system can be divided into the following three periods:the conventional performance analysis of the system, queueing game analysis of the system in the later in the20century, and take the customers’behavior as the mainly research direction at this time. Put differently, the mainly research realm transfers from the service provided part to the service calling part, which is the purpose of the analysis of the stochastic service system. The analysis of the customers’ behavior ultimately is the analysis of the service demand. The providing of the service drives by the service demand in essence. There is no need to provide service if it scare of service demand. Based on the elaborately analysis of the customers’behavior, we can obtain more important suggestion on the optimizing management, and it’s beneficial of the service system maximizing and the resource optimal allocation. This paper study the behavior of the different types of customers in the stochastic service system on the ground of the behavior analysis and make some suggestion on the purpose of maximizing control and maximizing risk margin decision.This thesis consists of five chapters.Chapter1gives a panorama of the stochastic service system and the background of the research, and then we give a brief introduction and research progress analysis of this topic.In chapter2, we introduce some basic concepts and results that are applied in our analysis throughout this thesis, including the common performance in the stochastic service system and the basic game theory, and we also introduce some common pricing methods, we also give an elaborate presentation on the pricing method that applied in this thesis.In chapter3, we consider the customers’behavior in an unreliable stochastic service system in the scenario of the risk neutral customers. To be specific, we take the fully observable and partially unobservable queue length scenarios as the basic background and study the selfish maximization, social welfare maximization and the revenue maximization of the service system. According to the analysis, we reach the following conclusions:in the same scenario, when customers are risk neutral and the expected sojourn time is convex respect to the customers’ joining strategies, the relative strategies satisfies the following:selfish maximizing strategy≥social maximizing strategy≥revenue maximizing strategy; In the different scenario, more information create more social welfare surplus, through monopoly in the stochastic service system may decrease the social welfare loss.In chapter4, having consider the customers’ risk preference, we further consider the behavior of the service system. Until now, we can comprehensively analysis this stochastic service system including the service provided part and the service calling part, and we further consider the risk tolerance of the system. Based on our analysis, we procured the following:customers behaves more aggressive with the service provider’s risk tolerance increases; when the risk tolerance lager than an critical value, the revenue of the provider decreases as the risk tolerance increases; on the contrary, the revenue of the provider increases as the risk tolerance increases. And we further obtain the optimal customers’ strategy and the optimal risk margin of the service system. This is our originality, for the previous paper only consider the unilateral behavior of the system and never consider the risk tolerance of the service system.In chapter5, we again conclude this paper, and give the relative suggestion on the optimal control. Based on the former pricing methods, we investigate another feasible pricing method, in order to procure the bilateral optimization, we use the pricing method combine the Perceived-Value Pricing Method and the Cost Plus Method (CPM) instead of the single pricing method.