Discrete-Event Simulation and Integer Linear Programming for Constraint-Aware Resource Scheduling

• Published in: IEEE transactions on systems, man, and cybernetics. Systems man, and cybernetics. Systems, 2018-09, Vol.48 (9), p.1578-1593
• Main Authors: Seung Yeob Shin, Brun, Yuriy, Balasubramanian, Hari, Henneman, Philip L., Osterweil, Leon J.
• Format: Article
• Language: English
• Subjects: Case studies; Complex systems; Computational modeling; Computer simulation; Decision analysis; Decision making; Discrete event systems; Discrete-event simulation (DES); Domains; Dynamic scheduling; Emergency medical services; Emergency procedures; human-intensive systems; Integer programming; Linear programming; Medical services; Processor scheduling; Resource management; resource planning; resource policy; Resource scheduling; Schedules; Specifications
• ISSN: 2168-2216; 2168-2232
• DOI: 10.1109/TSMC.2017.2681623

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 (LoS), 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 LoS 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.