EconPapers    
Economics at your fingertips  
 

Data-Driven Percentile Optimization for Multi-Class Queueing Systems with Model Ambiguity: Theory and Application

Austin Bren and Soroush Saghafian
Additional contact information
Austin Bren: Arizona State University
Soroush Saghafian: Harvard University

Working Paper Series from Harvard University, John F. Kennedy School of Government

Abstract: Multi-class queueing systems widely used in operations research and management typically experience ambiguity in real world settings in the form of unknown parameters. For such systems, we incorporate robustness in the control policies by applying a data-driven percentile optimization technique that allows for (1) expressing a controller’s optimism level toward ambiguity, and (2) utilizing incoming data in order to learn the true system parameters. We show that the optimal policy under the percentile optimization objective is related to a closed-form priority-based policy. We also identify connections between the optimal percentile optimization and cµ-like policies, which in turn enables us to establish effective but easy-to-use heuristics for implementation in complex systems. Using real-world data collected from a leading U.S. hospital, we also apply our approach to a hospital Emergency Department (ED) setting, and demonstrate the benefits of using our framework for improving current patient flow policies.

Date: 2018-02
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
https://research.hks.harvard.edu/publications/getFile.aspx?Id=1634

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ecl:harjfk:rwp18-008

Access Statistics for this paper

More papers in Working Paper Series from Harvard University, John F. Kennedy School of Government Contact information at EDIRC.
Bibliographic data for series maintained by ().

 
Page updated 2020-07-24
Handle: RePEc:ecl:harjfk:rwp18-008