Application of Quantum Annealing to Nurse Scheduling Problem
Abstract
Quantum annealing is a promising heuristic method to solve combinatorial optimization problems, and efforts to quantify performance on realworld problems provide insights into how this approach may be best used in practice. We investigate the empirical performance of quantum annealing to solve the Nurse Scheduling Problem (NSP) with hard constraints using the DWave 2000Q quantum annealing device. NSP seeks the optimal assignment for a set of nurses to shifts under an accompanying set of constraints on schedule and personnel. After reducing NSP to a novel Isingtype Hamiltonian, we evaluate the solution quality obtained from the DWave 2000Q against the constraint requirements as well as the diversity of solutions. For the test problems explored here, our results indicate that quantum annealing recovers satisfying solutions for NSP and suggests the heuristic method is potentially achievable for practical use. Moreover, we observe that solution quality can be greatly improved through the use of reverse annealing, in which it is possible to refine returned results by using the annealing process a second time. We compare the performance of NSP using both forward and reverse annealing methods and describe how this approach might be used in practice.
 Authors:

 Osaka Univ. (Japan)
 Univ. of Tennessee, Knoxville, TN (United States)
 Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
 Publication Date:
 Research Org.:
 Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
 Sponsoring Org.:
 USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC21)
 OSTI Identifier:
 1560436
 Grant/Contract Number:
 AC0500OR22725
 Resource Type:
 Accepted Manuscript
 Journal Name:
 Scientific Reports
 Additional Journal Information:
 Journal Volume: 9; Journal Issue: 1; Journal ID: ISSN 20452322
 Publisher:
 Nature Publishing Group
 Country of Publication:
 United States
 Language:
 English
 Subject:
 97 MATHEMATICS AND COMPUTING
Citation Formats
Ikeda, Kazuki, Nakamura, Yuma, and Humble, Travis S. Application of Quantum Annealing to Nurse Scheduling Problem. United States: N. p., 2019.
Web. doi:10.1038/s41598019491723.
Ikeda, Kazuki, Nakamura, Yuma, & Humble, Travis S. Application of Quantum Annealing to Nurse Scheduling Problem. United States. doi:10.1038/s41598019491723.
Ikeda, Kazuki, Nakamura, Yuma, and Humble, Travis S. Fri .
"Application of Quantum Annealing to Nurse Scheduling Problem". United States. doi:10.1038/s41598019491723. https://www.osti.gov/servlets/purl/1560436.
@article{osti_1560436,
title = {Application of Quantum Annealing to Nurse Scheduling Problem},
author = {Ikeda, Kazuki and Nakamura, Yuma and Humble, Travis S.},
abstractNote = {Quantum annealing is a promising heuristic method to solve combinatorial optimization problems, and efforts to quantify performance on realworld problems provide insights into how this approach may be best used in practice. We investigate the empirical performance of quantum annealing to solve the Nurse Scheduling Problem (NSP) with hard constraints using the DWave 2000Q quantum annealing device. NSP seeks the optimal assignment for a set of nurses to shifts under an accompanying set of constraints on schedule and personnel. After reducing NSP to a novel Isingtype Hamiltonian, we evaluate the solution quality obtained from the DWave 2000Q against the constraint requirements as well as the diversity of solutions. For the test problems explored here, our results indicate that quantum annealing recovers satisfying solutions for NSP and suggests the heuristic method is potentially achievable for practical use. Moreover, we observe that solution quality can be greatly improved through the use of reverse annealing, in which it is possible to refine returned results by using the annealing process a second time. We compare the performance of NSP using both forward and reverse annealing methods and describe how this approach might be used in practice.},
doi = {10.1038/s41598019491723},
journal = {Scientific Reports},
number = 1,
volume = 9,
place = {United States},
year = {2019},
month = {9}
}
Web of Science
Figures / Tables:
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