Document Detail

Online scheduling of pick-up and delivery tasks in hospitals.
MedLine Citation:
PMID:  19233317     Owner:  NLM     Status:  MEDLINE    
OBJECTIVE: The aim of this study was to develop an algorithm for scheduling pick-up and delivery tasks in hospitals. The number of jobs and the dynamic nature of the problem, in having jobs arriving over time, makes the use of information technology indispensable. An optimized scheduling for all types of transportation tasks occurring in a hospital accelerates medical procedures, and reduces the patient's waiting time and costs. METHODS: In the design of the algorithm we use techniques from classical scheduling theory. In addition, due to some special properties and constraints, we model the problem using methods from graph theory. The resulting algorithm combines both approaches in a transparent manner. CONCLUSIONS: To optimize the schedules, we define the average weighted flow time as an objective function that corresponds to a measure for the task throughput. An evaluation of the algorithm at the Natters State Hospital in Austria shows that it has a superior performance than the current scheduling mechanism.
Christian Fiegl; Carsten Pontow
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't     Date:  2009-02-20
Journal Detail:
Title:  Journal of biomedical informatics     Volume:  42     ISSN:  1532-0480     ISO Abbreviation:  J Biomed Inform     Publication Date:  2009 Aug 
Date Detail:
Created Date:  2009-07-06     Completed Date:  2009-09-22     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  100970413     Medline TA:  J Biomed Inform     Country:  United States    
Other Details:
Languages:  eng     Pagination:  624-32     Citation Subset:  IM    
Department of Computer Science, Swiss Federal Institute of Technology, ETH Zurich, Universitatsstrasse 6, 8092 Zurich, Switzerland.
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MeSH Terms
Models, Theoretical
Personnel Staffing and Scheduling*
Task Performance and Analysis
User-Computer Interface

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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