NSF Org:	CMMI Division of Civil, Mechanical, and Manufacturing Innovation
Recipient:	UNIVERSITY OF FLORIDA
Initial Amendment Date:	August 14, 2003
Latest Amendment Date:	August 14, 2003
Award Number:	0323664
Award Instrument:	Standard Grant
Program Manager:	Matthew Realff CMMI  Division of Civil, Mechanical, and Manufacturing Innovation ENG  Directorate for Engineering
Start Date:	September 1, 2003
End Date:	August 31, 2006 (Estimated)
Total Intended Award Amount:	$250,014.00
Total Awarded Amount to Date:	$250,014.00
Funds Obligated to Date:	FY 2003 = $250,014.00
History of Investigator:	Murray Cote (Principal Investigator) murray.cote@uchsc.edu Elif Akcali (Co-Principal Investigator)
Recipient Sponsored Research Office:	University of Florida 1523 UNION RD RM 207 GAINESVILLE FL  US  32611-1941 (352)392-3516
Sponsor Congressional District:	03
Primary Place of Performance:	University of Florida 1523 UNION RD RM 207 GAINESVILLE FL  US  32611-1941
Primary Place of Performance Congressional District:	03
Unique Entity Identifier (UEI):	NNFQH1JAPEP3
Parent UEI:
NSF Program(s):	SERVICE ENTERPRISE SYSTEMS
Primary Program Source:	app-0103
Program Reference Code(s):	1189, 9147, MANU
Program Element Code(s):	178700
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.041

ABSTRACT

This project aims to develop innovative capacity planning models for health care delivery. The research project will model a general health care system as a network of queuing stations and incorporate the queuing methodology into an optimization framework. The queuing reflects the unpredictable or random elements of health care systems, whereas the optimization framework captures tradeoffs between costs and levels of service. Capacity cost and performance functions are likely to be nonlinear, so the optimization will fall in the challenging category of nonlinear integer programming.

Capacity management is increasingly recognized as one of the critical issues in making health care delivery more flexible and cost-efficient. Resources must be allocated among levels of the delivery network (primary care clinics, acute care hospitals, rehab centers, nursing homes), as well as among units of these facilities. Successful development of optimization tools adapted to this task could have a tremendously positive impact on decision-making and facilitate better understanding of tradeoffs and their consequences. Furthermore, the project will foster development of interdisciplinary collaborations among engineering and hospital administration researchers and benefit from strong interactions with delivery facilities of three different types.

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