AI SearchNew model of an FMS using FTPN with demand variability and machine breakdown
Call Number: AIT Thesis no.ISE-09-31 Material type:
TextSeries: Asian Institute of Technology. Thesis ; no. ISE-09-31Publication details: Pathum Thani : Asian Institute of Technology, 2009Description: 47 p. : ill., chartsSubject(s): Online resources: Dissertation note: Thesis (M.Eng.) - Asian Institute of Technology, 2009 Summary: Fuzzy Timed Petri Nets (FTPN) have been used to model Flexible Manufacturing System (FMS). Effects of demand variability and machine breakdown on the performance measures of the system have been analyzed. At first, closed loop and decision free, generic Petri Net (PN) model of an FMS is extended, which has the properties of liveness and boundednes s. Then fuzzy Membership Functions (MF) of demand forecast, cycle time, machine availa bility, Work In Process (WIP) and demand satisfaction are considered. Then the effect s of demand variability and machine breakdown on cycle time, WIP, lead time, machine ut ilization and actual production of the system are analyzed through fuzzy inference of MF{u2019}s . The proposed model is verified and validated with t he help of an example model with two different cases. In 1 st case demand is considered minimum with maximum availability of machines, whereas, in 2 nd case demand is considered maximum with minimum availability of machines.
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A research study submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Industrial and Manufacturing Engineering, School of Engineering and Technology
Thesis (M.Eng.) - Asian Institute of Technology, 2009
Fuzzy Timed Petri Nets (FTPN) have been used to model Flexible Manufacturing System (FMS). Effects of demand variability and machine breakdown on the performance measures of the system have been analyzed. At first, closed loop and decision free, generic Petri Net (PN) model of an FMS is extended, which has the properties of liveness and boundednes s. Then fuzzy Membership Functions (MF) of demand forecast, cycle time, machine availa bility, Work In Process (WIP) and demand satisfaction are considered. Then the effect s of demand variability and machine breakdown on cycle time, WIP, lead time, machine ut ilization and actual production of the system are analyzed through fuzzy inference of MF{u2019}s . The proposed model is verified and validated with t he help of an example model with two different cases. In 1 st case demand is considered minimum with maximum availability of machines, whereas, in 2 nd case demand is considered maximum with minimum availability of machines.
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