Uniform-price auctions in staffing for self-scheduling service

Abstract

This research examines a uniform-price auction mechanism in managing staffing for self-scheduling business such as task sourcing and work-from-home call centers. We consider two types of service providers: Type-1 agents who require advanced notice before a shift starts and Type-2 agents who are flexible enough to be scheduled on-demand. We develop an integrated framework that can jointly analyze demand forecast, short-term scheduling, and long-term planning of staff capacity. We discuss the adoption of a blended workforce in scheduling and the implication of attrition costs in the long-term staffing. In addition, we compare the auction model with a popular fixed-wage model, in order to examine under what conditions the auction model is preferred. These results provide insights to staff managers on the choice of staffing and wage models.

Keywords:

• Self-scheduling service
• auction
• staffing and scheduling
• fixed-wage

Acknowledgments

The authors are grateful to Ying Li and William E. Stein for their discussion and help in coding in the early phase of the research.

Additional information

Notes on contributors

Yanling Chang

Yanling Chang is an assistant professor in the Department of Engineering Technology and Industrial Distribution and the Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, USA. She received her Bachelor’s degree in Electronic and Information Science and Technology from Peking University, Beijing, China; the Master's degree in Mathematics and the Ph.D. degree in Operations Research from Georgia Institute of Technology, Atlanta, GA, USA.

Lu Sun

Lu Sun is a doctoral student in the Department of Industrial and Systems Engineering, Texas A&M University. She obtained her Bachelor’s degree in mathematics from Beihang University, China.

Matthew F. Keblis

Matthew F. Keblis is an associate professor in the Management Department at the United States Coast Guard Academy. Prior to joining the Coast Guard Academy, he was on the faculties of Texas A&M University, Macquarie University, the University of Dallas and the University of Wyoming. He also has been an employee of or served as a consultant to a number of organizations including the US Navy, Amazon.com, Toyota and RAND Corporation. He received his AB degree in economics from the University of Chicago, his MS degree in operations research from the Illinois Institute of Technology and his PhD in industrial and operations engineering from the University of Michigan.

Jie Yang

Jie Yang was a postdoctoral researcher in Texas A&M University. She obtained her PhD degree in management science and engineering from Tianjin University, China.