Rescheduling of elective patients upon the arrival of emergency patients

Authors:
Ergin Erdem;Xiuli Qu;Jing Shi
Affiliations:
Department of Industrial and Manufacturing Engineering, North Dakota State University, Fargo, ND 58108, USA;Department of Industrial and Systems Engineering, North Carolina A&T State University, 1601 E. Market Street, Greensboro, NC 27411, USA;Department of Industrial and Manufacturing Engineering, North Dakota State University, Fargo, ND 58108, USA
Venue:
Decision Support Systems
Year:
2012

Citing 12
Cited 0

Optimization of control parameters for genetic algorithms

IEEE Transactions on Systems, Man and Cybernetics
Reducing surgical patient costs through use of an artificial neural network to predict transfusion requirements

Decision Support Systems
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
A decision support tool for allocating hospital bed resources and determining required acuity of care

Decision Support Systems
An analysis of the behavior of a class of genetic adaptive systems.

An analysis of the behavior of a class of genetic adaptive systems.
The use of simulation to determine maximum capacity in the surgical suite operating room

Proceedings of the 38th conference on Winter simulation
Closing Emergency Operating Rooms Improves Efficiency

Journal of Medical Systems
Sequencing surgical cases in a day-care environment: An exact branch-and-price approach

Computers and Operations Research
Introduction to Genetic Algorithms

Introduction to Genetic Algorithms
Operating theatre scheduling with patient recovery in both operating rooms and recovery beds

Computers and Industrial Engineering
An analytic approach to better understanding and management of coronary surgeries

Decision Support Systems
A mean-variance model to optimize the fixed versus open appointment percentages in open access scheduling systems

Decision Support Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this study, a mixed integer linear programming (MILP) model is developed for rescheduling elective patients upon the arrival of emergency patients by considering two types of clinical units, namely operating rooms and post-anesthesia care units (PACUs). The model considers the overtime cost of the operating rooms and/or the PACUs, the cost of postponing or preponing elective surgeries, and the cost of turning down the emergency patients. The results indicate that a mainstream commercial solver can efficiently find an optimal solution in a particular scenario with light elective surgery load, but becomes very inefficient in searching optimal solutions in all other scenarios. As such, a genetic algorithm is developed to efficiently obtain the approximately optimal solutions in those scenarios that are difficult for the commercial solver. In the genetic algorithm, a novel chromosome structure is proposed and applied to represent the feasible solutions to the MILP model. It is shown that for the scenarios with heavy load of elective surgeries, the genetic algorithm can find approximate optimal solutions significantly faster than the commercial solver. In practice, the two solution methodologies should be used jointly to provide hospitals a solid tool for making sound and timely decisions in admitting emergency patients and rescheduling elective patients.