Paper Info
Title
A sequential statistics approach to dynamic staffing under demand uncertainty
Abstract
Service systems are highly dependent on staffing decisions to provide satisfactory quality of service. This paper tackles the problem of decision making under uncertainty pertaining to the source of demand. Regardless of the distribution of the demand, the proposed staffing rule reacts to the requested quality of service to determine the quality of the estimators of the unknown demand-process parameters, as well as making optimal staffing decisions. Theoretical results on the consistency and optimality of the proposed method is illustrated using sequential statistics approaches.
Year
DOI
Venue
2017
10.5555/3242181.3242255
WSC '17: Winter Simulation Conference Las Vegas Nevada December, 2017
Keywords
Field
DocType
demand uncertainty,service systems,decision making,sequential statistics approaches,service quality,dynamic staffing rule
Random variable,Staffing,Computer science,Quality of service,Statistics,Estimator
Conference
ISSN
ISBN
Citations 
0891-7736
978-1-5386-3427-1
0
PageRank 
References 
Authors
0.34
7
2
Name
Order
Citations
PageRank
Fatemeh S. Hashemi120.70
Michael R. Taaffe26417.75