Two Basic Queueing Models of Service Platforms in Digital Sharing Economy

TL;DR

This paper develops two queueing models for digital sharing economy service platforms using QBD processes, analyzing stability, performance metrics, and profits, with numerical verification and implications for system optimization.

Contribution

Introduces two novel queueing models for service platforms in the sharing economy using QBD processes, enabling detailed performance analysis and future research directions.

Findings

Models expressed as level-independent QBD processes

Derived system stability and performance measures

Numerical examples confirm theoretical results

Abstract

This paper describes two basic queueing models of service platforms in digital sharing economy by means of two different policies of platform matching information. We show that the two queueing models of service platforms can be expressed as the level-independent quasi birth-and-death (QBD) processes. Using the proposed QBD processes, we provide a detailed analysis for the two queueing models of service platforms, including the system stability, the average stationary numbers of seekers and of idle owners, the expected sojourn time of an arriving seeker, and the expected profits for both the service platform and each owner. Finally, numerical examples are employed to verify our theoretical results, and demonstrate how the performance measures of service platforms are influenced by some key system parameters. We believe that the methodology and results developed in this paper not only can be applied to develop a broad class of queuing models of service platforms, but also will open a series of promising innovative research on performance evaluation, optimal control and queueing-game of service platforms and digital sharing economy.