Temporary recruitment is a fast growing labor market with candidates, usually professionals, looking for contract positions in today’s fast evolving and dynamic economy. While two-sided matching algorithms have been applied to some labor markets during the past decades, they usually assume a static environment in which the strategic interactions between candidates and employers are designed without considering dynamic changes in market participants. In recent years, the advance of Internet and mobile technologies has enabled the implementation of large scale online labor markets which can capture the dynamics of the participants in a near real time manner.

In this thesis, I study how to improve the effectiveness and the efficiency of recruitment processes in the context of temporary labor markets. The key challenge is how to design two-sided matching algorithms to accommodate dynamic changes in market participants. To have a better understanding of the challenge and possible solutions, I first analyzed the problem using the Environment-Based Design (EBD) methodology. The analyzing results show that two aspects need to by improved in the proposed two sided matching algorithm: the efficiency, which is the responsiveness of the algorithm, and the effectiveness, which is measured by the consistency of solutions generated by the algorithm when dynamics are introduced.

Based on the results derived from EBD analysis, I designed two algorithms to improve the efficiency and the effectiveness. For the efficiency, I designed a request-accumulated deferred acceptance algorithm, which is a modification of the classical deferred acceptance algorithm. In addition, I designed a repair-based two-sided matching algorithm to improve solution consistency when changes are introduced to the market. The performance of the proposed algorithms are evaluated through a computational study. The results show that the efficiency and the effectiveness design requirements are satisfied.