Discrete-Event Simulation and Integer Linear Programming for Constraint-Aware Resource Scheduling
by Seung Yeob Shin, Yuriy Brun, Hari Balasubramanian, Philip L. Henneman, Leon J. Osterweil
Abstract:
This paper presents a method for scheduling resources in complex systems that integrate humans with diverse hardware and software components, and for studying the impact of resource schedules on system characteristics. The method uses discrete-event simulation and integer linear programming, and relies on detailed models of the system's processes, specifications of the capabilities of the system's resources, and constraints on the operations of the system and its resources. As a case study, we examine processes involved in the operation of a hospital emergency department, studying the impact staffing policies have on such key quality measures as patient length of stay, number of handoffs, staff utilization levels, and cost. Our results suggest that physician and nurse utilization levels for clinical tasks of 70% result in a good balance between length of stay and cost. Allowing shift lengths to vary and shifts to overlap increases scheduling flexibility. Clinical experts provided face validation of our results. Our approach improves on the state of the art by enabling using detailed resource and constraint specifications effectively to support analysis and decision making about complex processes in domains that currently rely largely on trial and error and other ad hoc methods.
Citation:
Seung Yeob Shin, Yuriy Brun, Hari Balasubramanian, Philip L. Henneman, and Leon J. Osterweil, Discrete-Event Simulation and Integer Linear Programming for Constraint-Aware Resource Scheduling, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 47, 2017.
Related:
Previous version appeared as University of Massachusetts Computer Science technical report UM-CS-2014-009.
Bibtex:
@article{Shin17tsmc,
  author = {{Seung Yeob} Shin and Yuriy Brun and Hari Balasubramanian and
  Philip L. Henneman and Leon J. Osterweil},
  title ={\href{http://people.cs.umass.edu/brun/pubs/pubs/Shin17tsmc.pdf}{Discrete-Event 
  Simulation and Integer Linear Programming for Constraint-Aware Resource Scheduling}},
  journal = {IEEE Transactions on Systems, Man, and Cybernetics: Systems},
  venue = {TSMC},
  year = {2017},
  volume = {47},

  abstract = {This paper presents a method for scheduling resources in
  complex systems that integrate humans with diverse hardware and software
  components, and for studying the impact of resource schedules on system
  characteristics. The method uses discrete-event simulation and integer
  linear programming, and relies on detailed models of the system's
  processes, specifications of the capabilities of the system's resources,
  and constraints on the operations of the system and its resources. As a
  case study, we examine processes involved in the operation of a hospital
  emergency department, studying the impact staffing policies have on such
  key quality measures as patient length of stay, number of handoffs, staff
  utilization levels, and cost. Our results suggest that physician and nurse
  utilization levels for clinical tasks of 70% result in a good balance
  between length of stay and cost. Allowing shift lengths to vary and shifts
  to overlap increases scheduling flexibility. Clinical experts provided face
  validation of our results. Our approach improves on the state of the art by
  enabling using detailed resource and constraint specifications effectively
  to support analysis and decision making about complex processes in domains
  that currently rely largely on trial and error and other ad hoc methods.},

  doi = {10.1109/TSMC.2017.2681623},
  note = {Previous version appeared as University of Massachusetts Computer
  Science technical report UM-CS-2014-009, 
  \href{http://dx.doi.org/10.1109/TSMC.2017.2681623}{DOI: 10.1109/TSMC.2017.2681623}},
  previous = {Previous version appeared as University of Massachusetts Computer
  Science technical report UM-CS-2014-009.},

  fundedBy = {NSF IIS-1239334, NSF CMMI-1234070, CMMI-1254519},
}