Flexible cyclic rostering in the service industry

ABSTRACT

Companies in the service industry frequently depend on cyclic rosters to schedule their workforce. Such rosters offer a high degree of fairness and long-term predictability of days on and off, but they can hinder an organization’s ability to respond to changing demand. Motivated by the need for improving cyclic planning at an airport ground handling company, this article introduces the idea of flexible cyclic rostering as a means of accommodating limited weekly adjustments of employee schedules. The problem is first formulated as a multi-stage stochastic program; however, this turned out to be computationally intractable. To find solutions, two approximations were developed that involved reductions to a two-stage problem. In the computational study, the flexible and traditional cyclic rosters derived from these approximations are compared and metrics associated with the value of stochastic information are reported. In the testing, we considered seven different perturbations of the demand curve that incorporate the types of uncertainty that are common throughout the service industry. To the best of our knowledge, this is the first analysis of cyclic rostering that applies stochastic optimization. The results show that a reduction in undercoverage of more than 10% on average can be achieved with minimal computational effort. It was also observed that the new approach can overcome most of the limitations of traditional cyclic rostering while still providing most of its advantages.

Keywords:

• Cyclic rostering
• flexible cyclic rostering
• stochastic programming
• two-stage stochastic optimization

Additional information

Notes on contributors

Ferdinand Kiermaier

Ferdinand Kiermaier is working in a post-doc position at BASF as part of the Commodity Risk Management team in global procurement focusing on data analytics. In addition, he teaches at the Technical University of Munich and is co-founder of the analytics and optimization company AOS. He received his M.Sc. in Computer Science as well as his Ph.D. at the Technical University of Munich. His main research is related to service and supply chain operations management considering problems in airport operations, workforce scheduling, vehicle routing, and multi-echelon optimization.

Markus Frey

Markus Frey has received his Ph.D. at the Technical University of Munich. His main interests are problems related to service operations management, an area in which he has published several papers. In addition to his work at the Technical University of Munich as researcher and private docent, he works as senior scientist for operations research at BASF in the Department Global Business Analytics–Optimization. Moreover, he is co-founder of the optimization company AOS.

Jonathan F. Bard

Jonathan F. Bard is a Professor of Operations Research & Industrial Engineering in the Mechanical Engineering Department at the University of Texas at Austin. He holds the Industrial Properties Corporation Endowed Faculty Fellowship and serves as the Associate Director of the Center for the Management of Operations and Logistics. He received a D.Sc. in Operations Research from The George Washington University. His research centers on improving healthcare delivery, personnel scheduling, production planning and control, and the design of decomposition algorithms for solving large-scale optimization problems, and has appeared in a wide variety of technical journals. Currently, he serves on six editorial boards and previously was a Focused Issue Editor of IIE Transaction and an Associate Editor of Management Science. He is a registered engineer in the State of Texas, a fellow of IIE and INFORMS, and a senior member of IEEE. In the past, he has held a number of offices in each of these organizations and is currently the INFORMS Vice President of Publications.