CLC number: TP391; O226
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2018-03-15
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Chun-hua He. Tabu search based resource allocation in radiological examination process execution[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(3): 446-458.
@article{title="Tabu search based resource allocation in radiological examination process execution",
author="Chun-hua He",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="19",
number="3",
pages="446-458",
year="2018",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1601802"
}
%0 Journal Article
%T Tabu search based resource allocation in radiological examination process execution
%A Chun-hua He
%J Frontiers of Information Technology & Electronic Engineering
%V 19
%N 3
%P 446-458
%@ 2095-9184
%D 2018
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1601802
TY - JOUR
T1 - Tabu search based resource allocation in radiological examination process execution
A1 - Chun-hua He
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 19
IS - 3
SP - 446
EP - 458
%@ 2095-9184
Y1 - 2018
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1601802
Abstract: Efficient resource scheduling and allocation in radiological examination process (REP) execution is a key requirement to improve patient throughput and radiological resource utilization and to manage unexpected events that occur when resource scheduling and allocation decisions change due to clinical needs. In this paper, a tabu search based approach is presented to solve the resource scheduling and allocation problems in REP execution. The primary objective of the approach is to minimize a weighted sum of average examination flow time, average idle time of the resources, and delays. Unexpected events, i.e., emergent or absent examinations, are also considered. For certain parameter combinations, the optimal solution of radiological resource scheduling and allocation is found, while considering the limitations such as routing and resource constraints. Simulations in the application case are performed. Results show that the proposed approach makes efficient use of radiological resource capacity and improves the patient throughput in REP execution.
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