1
Due-date tightness is high
µ
1.0 0
10.0 Due-date tightness 1
Due-date tightness is low
rule is calculated on the basis of both areas S1 and S2. For example, when K is 6 and the machine center utilization level is 90%, Figures 5.11 and 5.12, the final decision rule is determined as follows:
(Priority with LVF)n, Equation (5.38)
where j = queue number
n = part number in the preceding queue of machine center j (or simply queue j) LVF = low value first
(IOT)n,j = imminent operation time of part n in queue j
(SLRO)n,j = slack/remaining number of operation (SLACK/RO) of part n in queue j
It can be seen from Equation 5.38 that when S2 is zero, priority = IOT, i.e., SIO rule applies, and when S1 is zero then priority = SLRO, i.e., SLACK/RO rule applies.
Defining Terms
AGV Automatic guided vehicle
AI Artificial intelligence
CLSD Closest distance
CYC Cyclic
EDD Earliest due date
FCFS First come first served
FIFO First in first out
FIGURE 5.11 Conclusion modification for rule 1.
FIGURE 5.12 Conclusion modification for rule 2.
1.0 0
9.0 K
6.0 10
0
100% L
90% 1
0
Due-date tightness
µ
1.0 0
9.0 K 1
6.0
µ
10%
0
100% L 1
90%
µ
1 0
Due-date tightness 1
S2 A2
j S S
j n j j n j
=
( )
1 ×( )
IOT , +( )
2 ×(
SLRO)
,LULIB Lowest utilization of local input buffer
MAW Modified additive weighting
MCD Multi-criterion decision making
NINQ Number of parts in queue
PM Performance measure
POR Preferred order
RAN Random
SAW Simple additive weighting
SDS Shortest distance to station
SIO Shortest imminent operation time
SIOx or SIx Truncated SIO SLACK Shortest remaining slack time
SLACK/RO Slack per number of remaining operations SLRO Ratio of slack to remaining operation time
SNQ Shortest number in queue
SPT Shortest processing time
SRPT Shortest remaining processing time STPT Shortest total processing time
TWK Total work
WINQ Work in queue
WIP Work in process
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For Further Information
Jamshidi, M., Vadaiee, N. and Ross T. J. 1993, Fuzzy Logic and Control: Software and Hardware Applications, Prentice-Hall, Englewood Cliffs, NJ.
Parsaei, H. R. 1995, Design and Implementation of Intelligent Manufacturing Systems: From Expert Systems, Neural Networks, to Fuzzy Logic, Prentice-Hall, Englewood Cliffs, NJ.
Chen, C. H. 1996, Fuzzy Logic and Neural Network Handbook, McGraw-Hill, New York.