AI - a Guide to Intelligent Systems.pdf - Member of EEPIS

BUILDING A FUZZY EXPERT SYSTEM
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A typical process in developing the fuzzy expert system incorporates the
following steps:
1. Specify the problem and define linguistic variables.
2. Determine fuzzy sets.
3. Elicit and construct fuzzy rules.
4. Encode the fuzzy sets, fuzzy rules and procedures to perform fuzzy inference
into the expert system.
5. Evaluate and tune the system.
Step 1:
Specify the problem and define linguistic variables
The first, and probably the most important, step in building any expert
system is to specify the problem. We need to describe our problem in
terms of knowledge engineering. In other words, we need to determine
problem input and output variables and their ranges.
For our problem, there are four main linguistic variables: average
waiting time (mean delay) m, repair utilisation factor of the service
centre , number of servers s, and initial number of spare parts n.
The customer’s average waiting time, m, is the most important
criterion of the service centre’s performance. The actual mean delay
in service should not exceed the limits acceptable to customers.
The repair utilisation factor of the service centre, , is the ratio of the
customer arrival rate, , to the customer departure rate, . Magnitudes
of and indicate the rates of an item’s failure (failures per unit time)
and repair (repairs per unit time), respectively. Apparently, the
repair rate is proportional to the number of servers, s. To increase
the productivity of the service centre, its manager will try to keep the
repair utilisation factor as high as possible.
The number of servers, s, and the initial number of spares, n, directly
affect the customer’s average waiting time, and thus have a major
impact on the centre’s performance. By increasing s and n, we achieve
lower values of the mean delay, but, at the same time we increase the
costs of employing new servers, building up the number of spares and
expanding the inventory capacities of the service centre for additional
spares.
Let us determine the initial number of spares n, given the customer’s
mean delay m, number of servers s, and repair utilisation factor, .
Thus, in the decision model considered here, we have three inputs –
m, s and , and one output – n. In other words, a manager of the service
centre wants to determine the number of spares required to maintain
the actual mean delay in customer service within an acceptable range.
Now we need to specify the ranges of our linguistic variables.
Suppose we obtain the results shown in Table 4.3 where the intervals
for m, s and n are normalised to be within the range of ½0; 1Š by dividing
base numerical values by the corresponding maximum magnitudes.