Applying Deep Reinforcement Learning to Dynamic Staff Scheduling in a Service System with Nonstationary Arrival Process; a Case Study of Grocery Store

Samira Nichols,Konstantinos Mykoniatis,Brigid Otoo

doi:10.2139/ssrn.4276480

Applying Deep Reinforcement Learning to Dynamic Staff Scheduling in a Service System with Nonstationary Arrival Process; a Case Study of Grocery Store

Samira Nichols, Konstantinos Mykoniatis + Show 1 more

https://doi.org/10.2139/ssrn.4276480

Copy DOI

Export

Save

Cite

Journal: SSRN Electronic Journal

Publication Date: Jan 1, 2022

Affiliation: University of Central Arkansas, Auburn University

#Nonstationary Arrival Process #Service System #Dynamic Scheduling #Deep Learning #Nonstationary Arrival #Deep Reinforcement Learning #Staff Scheduling #Grocery Store #Arrival Process #Reinforcement Learning

Abstract
Full-Text
Similar Papers

Abstract

Listen

Applying Deep Reinforcement Learning to Dynamic Staff Scheduling in a Service System with Nonstationary Arrival Process; a Case Study of Grocery Store

Full Text

Published Version

Check institute access

Open DOI Link

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Similar Papers

Paper Title

Journal

Date

Author

View more papers

More From: SSRN Electronic Journal

Paper Title

Journal

Date

Author

View more papers

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.

R Discovery Prime

Applying Deep Reinforcement Learning to Dynamic Staff Scheduling in a Service System with Nonstationary Arrival Process; a Case Study of Grocery Store