9300568 Foote The advancement in computer technology and the competition among computer companies have resulted in an increase in the application of computer aided design (CAD), computer aided process planning and computer aided manufacturing (CAM). Although the development of new technologies is necessary for the advancement of science, lack of integration of these technologies will prove detrimental to the system and hence may result in the desertion of such technologies. Today, there is greater need for efficient production management techniques than ever. Current methods of improving production management focus on several approaches as independent methods of improvement. The goal of this research is to develop in some sense a 'maximal' production management methodology. A 'maximal' methodology is one that takes advantages of the major variable sources of flexibility and response available to a manager: choosing lot size,process plan (which part feature to process), process used to create feature, sequence at each process, inventory created, shortage accepted or subcontract awarded. The methodology applies to a hybrid assembly job shop, which manufactures components some of which go to an assembly line. This approach is now possible due to advances in modeling and computational methodologies, increased computer power, and advances in data base organization. The individual data fields (time standards, process plans, holding costs, reliability and defect rate data, etc.) are limited to those currently available in actual manufacturing systems known and explored by the researchers. The individual choices must be coordinated since optimizing an individual variable such as lot size may overload a bottleneck resource, optimizing flow time by shortest processing time may increase tardiness and again overload a temporary bottleneck. The integration of fast simulation process planning, multiple criteria optimization, stochastic programming with recourse techniques with the scheduling and sequencing techniques is original and shows promise of improvements over the current theory.

Agency
National Science Foundation (NSF)
Institute
Division of Civil, Mechanical, and Manufacturing Innovation (CMMI)
Application #
9300568
Program Officer
Lawrence M. Seiford
Project Start
Project End
Budget Start
1993-09-15
Budget End
1997-02-28
Support Year
Fiscal Year
1993
Total Cost
$157,700
Indirect Cost
Name
University of Oklahoma
Department
Type
DUNS #
City
Norman
State
OK
Country
United States
Zip Code
73019