Multi-Phase and Integrated Multi-Objective Cyclic Operating Room Scheduling Based on an Improved NSGA-II Approach

The operating room (OR) is an important department in a hospital, and the scheduling of surgeries in ORs is a challenging combinatorial optimization problem. In this paper, we address the problem of multiple resource allocation of ORs and propose a surgery scheduling scheme for OR units. To solve th...

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Bibliographic Details
Published in:Symmetry (Basel) Vol. 11; no. 5; p. 599
Main Authors: Lu, Qian, Zhu, Xiaomin, Wei, Dong, Bai, Kaiyuan, Gao, Jinsheng, Zhang, Runtong
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.05.2019
Subjects:
Combinatorial analysis
Genetic algorithms
Hospitals
Integer programming
Linear programming
Mathematical programming
Medical research
Multiphase
Optimization
Patient satisfaction
Phase (cyclic)
Resource allocation
Schedules
Scheduling
Sorting algorithms
Surgery
ISSN:2073-8994, 2073-8994
Online Access:Get full text
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Summary:The operating room (OR) is an important department in a hospital, and the scheduling of surgeries in ORs is a challenging combinatorial optimization problem. In this paper, we address the problem of multiple resource allocation of ORs and propose a surgery scheduling scheme for OR units. To solve this problem, a multi-phase and integrated multi-objective linear programming model is proposed. The first phase of the proposed model is a resource allocation model, which mainly focuses on the allocation of ORs for each surgical specialty (SS). Based on the results of the first phase, the second phase is the cyclic Master Surgical Schedule model, which aims to schedule the surgeries in each SS. The proposed models are solved by the Non-dominated Sorting Genetic Algorithm II (NSGA-II), which was improved. Finally, two numerical experiments based on practical data are provided to verify the effectiveness of the proposed models as well as to evaluate the performance of the improved NSGA-II. Our final results illustrate that our proposed model can provide hospital managers with a series of “optimal” solutions to effectively allocate relevant resources and ORs for surgeries, and they show that the improved NSGA-II has high computational efficiency and is more suitable in solving larger-scale problems.
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ISSN:2073-8994
2073-8994
DOI:10.3390/sym11050599