The optimal operation decisions of expert service enterprises based on queuing system

Expert service enterprises constitute an important part of the service sector and play a significant role in its development. However, such enterprises face two major challenges. On the one hand, they must satisfy heterogeneous customer demand for experts of different ranks; on the other hand, they must manage compensation constraints in the expert labor market. To address these challenges, this study develops a queueing-theoretic framework that captures the joint effects of expert rank and service time. In doing so, it explicitly endogenies expert rank in the model and examines how enterprises optimize expert rank and service time under different demand scenarios. The results yield several key findings. First, the optimal expert rank chosen by the enterprise is independent of the overall demand volume, which is a somewhat unexpected result. This finding suggests that expert recruitment should be aligned with the firm's long-term strategic positioning rather than adjusted merely in response to short-term demand fluctuations. Second, when market demand is strong, the enterprise can adopt a "low-margin, high-volume" strategy by jointly optimizing service time and service price, thereby expanding customer coverage and maximizing service throughput. Third, when demand is relatively low, the enterprise should carefully adjust service price and expert compensation to maintain profitability without compromising service quality. Overall, this study provides practical managerial insights for expert service enterprises seeking to optimize service delivery strategies, balance customer expectations with expert compensation, and avoid market distortions. By endogenizing expert rank and service time and analyzing equilibrium decision behavior, this study also extends the research perspective on expert service operations.