Ap p li c a t i on of Fu z z y Nu mb er M a x -Plu s Alg eb ra to Closed Serial Queuing Network… 197
IR
,
,
is called interval max-plus algebra, and is written as IR
max
. Relation “
Im
”defined on IR
max
as x
Im
y x
y = y is a partial order on IR
max
. Notice that x
y = y
x
m
y
and
x
m
y
.
Define IR
n m
max
:= {A = A
ij
A
ij
IR
max
, i = 1, 2, ..., m, j = 1, 2, ..., n}. The
elements of IR
n m
max
are called matrices over interval max-plus algebra or shortly interval matrices
. The operations on interval matrices can be defined in the same way with the operations on matrices over max-plus algebra. For any matrix A
IR
n m
max
, Define the matrix
A
=
ij
A
R
n m
max
and
A
=
ij
A
R
n m
max
, which are called lower bound matrix and upper bound matrix of A, respectively. Define a matrix interval of A, that is [
A
,
A
] = {A
R
n m
max
A
m
A
m
A
} and I
n m
max
R
b
= { [
A
,
A
] A R
n n
max
}. The matrix interval [
A
,
A
] and [
B
,
B
] IR
n m
max
b
are equal if
A
=
B
and
A
=
B
. We can show that for every matrix interval A
IR
n m
max
we can determine matrix interval [
A
,
A
] I
n m
max
R
b
and conversely. The matrix interval [
A
,
A
] is called matrix interval associated with the interval
matrix A, and is written as A [
A
,
A
]. Moreover, we have A [
A
,
A
], A
B [
A
B
,
A
B
] and A
B [
A
B
,
A
B
].
Define IR
n max
:= { x = [x
1
, x
2
, ... , x
n
]
T
| x
i
IR
max
, i = 1, 2, ... , n }. Note that
I R
n max
can be viewed as IR
1 max
n
. The elements of IR
n max
are called interval vectors over
I R
max
or shortly interval vectors. An interval vector x IR
n max
is said to be not equal to interval vector
, and is written as x , if there exists i {1, 2, ..., n} such that x
i
. Interval matrix A
IR
n n
max
, where A [
A
,
A
], is said to be irreducible if every matrix A
[
A
,
A
] is irreducible. We can show that interval matrix A
IR
n n
max
, where A
[
A
,
A
] is irreducible if and only if
A
n n
max
R
is irreducible Rudhito, et al. [7].
4. FUZZY NUMBER MAX-PLUS ALGEBRA
In this section we will review some concepts and results of fuzzy number max-plus algebra, matrix over fuzzy number max-plus algebra and fuzzy number max-plus eigenvalue.
Further details can be found in Rudhito [8]. Fuzzy set
K ~
in universal set X is represented as the set of ordered pairs
K ~
= {x,
K ~
x x X } where
K ~
is a membership function of fuzzy set
K ~
, which is a mapping
198 M.
A
NDY
R
UDHITO
E
T
A
L
from universal set X to closed interval [0, 1]. Support of a fuzzy set
K ~
is supp
K ~
= {x X
K ~
x 0}. Height of a fuzzy set
K ~
is height
K ~
=
X x
sup
{
K ~
x}. A fuzzy set
K ~
is said to be normal if height
K ~
= 1. For a number
[0, 1],
-cut of a fuzzy set
K ~
is cut
K ~
=
K
={x X
K ~
x
}. A fuzzy sets
K ~
is said to be convex if
K
is convex, that is contains line segment between any two points in the
K
, for every
[0, 1], Fuzzy number
a ~
is defined as a fuzzy set in universal set R which satisfies the
following properties: i normal, that is a
1
, ii for every
0, 1] a
is closed in R, that
is there exists
a
,
a
R with
a
a
such that a
= [
a
,
a
] = {x
R
a
x
a
}, iii supp
a ~
is bounded. For
= 0, define a = [infsupp
a ~
, supsupp
a ~
]. Since
every closed interval in R is convex, a
is convex for every
[0, 1], hence
a ~
is convex.
Let FR
~
:= FR {
~
}, where FR is set of all fuzzy numbers and
~
: = { }
with the
- cut of
~
is
= [ ,]. Define two operations
~
and
~
such that for every
a ~
,
b ~
FR
~
, with a
= [
a
,
a
] IR
max
and b
= [
b
,
b
] IR
max
, i
Maximum of
a ~
and
b ~
, written
a ~
~
b ~
, is a fuzzy number whose
- cut is interval
[
a
b
,
a
b
] for every
[0, 1] ii
Addition of
a ~
and
b ~
, written
a ~
~
b ~
, is a fuzzy number whose
- cut is interval
[
a
b
,
a
b
] for every
[0, 1].
We can show that FR
~
,
~
,
~
is a commutative idempotent semiring. The
commutative idempotent semiring FR
max
:= FR
~
,
~
,
~
is called fuzzy number max- plus algebra
, and is written as FR
max
Rudhito, et al. [8].
Define FR
n m
max
:= {
A ~
=
A ~
ij
A ~
ij
FR
max
, i = 1, 2, ..., m and j = 1, 2, ..., n }. The
elements of FR
n m
max
are called matrices over fuzzy number max-plus algebra or shortly fuzzy number matrices.
The operations on fuzzy number matrices can be defined in the same way with the operations on matrices over max-plus algebra. Define matrix
E ~
FR
n n
max
, with
E ~
ij
: =
j
i ~
j i
~ if
if
, and matrix
~
FR
n n
max
, with
~
ij
:=
~
for every i and j.
For every
A ~
FR
n m
max
and
[0, 1], define
-cut matrix of
A ~
as the interval matrix A
=
ij
A
, with
ij
A
is the -cut of
A ~
ij
for every i and j. Define matrix
A
=
ij
A
n m
max
R
and
A
=
ij
A
n m
max
R
which are called lower bound and upper bound of matrix
A
, respectively .
We can conclude that the matrices
A ~
,
B ~
FR
n m
max
are equal iff A
= B
,
Ap p li c a t i on of Fu z z y Nu mb er M a x -Plu s Alg eb ra to Closed Serial Queuing Network… 199
that is
ij
A
=
ij
B
for every
[0, 1] and for every i and j. For every fuzzy number matrix
A ~
, A
[
A
,
A
]. Let
~
FR
max
,
A ~
,
B ~
FR
n m
max
. We can show that
A
[
A
,
A
] and A B
[
A
B
,
A
B
] for every
[0, 1]. Let
A ~
FR
p m
max
,
A ~
FR
n p
max
. We can show that A B
[
A
B
,
A
B
] for every
[0, 1].
Define FR
n max
:= {
x
~
= [
1
x ~
,
2
x ~
, ... ,
n
x ~
]
T
|
i
x ~
FR
max
, i = 1, ... , n }. The
elements in FR
n max
are called fuzzy number vectors over FR
max
or shortly fuzzy number vectors
. A fuzzy number vector
x
~
FR
n max
is said to be not equal to fuzzy number vector
ε~
, written
x
~
ε~
, if there exists i {1, 2, ..., n} such that
i
x ~
~
. Fuzzy number matrix
A ~
FR
n n
max
is said to be irreducible if A
IR
n n
max
is irreducible for every
[0, 1]. We can show that
A ~
is irreducible if and only if
A
R
n n
max
is irreducible Rudhito, et al. [7]. Let
A ~
FR
n n
max
. The fuzzy number scalar
~
FR
max
is called fuzzy number max- plus eigenvalue
of matrix
A ~
if there exists a fuzzy number vector
v
~
FR
n max
with
v
~
ε~
n 1
such that
A ~
~
v
~
=
~ ~
v
~
.
The vector
v
~
is called fuzzy number max-plus eigenvectors of matrix
A ~
associaed with
~
.
Given
A ~
FR
n n
max
. We can show that the fuzzy number scalar
max
~
A ~
=
1] [0,
max
~ , where
max
~
is a fuzzy set in R with membership function
max
~
x =
max
~
x, and
max
~
is the characteristic function of the set [
max
A
,
max
A
], is a fuzzy number max-plus eigenvalues of matrix
A ~
. Based on fundamental max-plus eigenvector associated with eigenvalues
max
A
and
max
A
, we can find fundamental fuzzy number max-plus eigenvector
associated with
max
~
Rudhito [8]. Moreover, if matrix
A ~
is irreducible, then
max
~
A ~
is the unique fuzzy number max-plus eigenvalue of matrix
A ~
and the fuzzy number max-plus eigenvector associated with
max
A ~
is
v
~
, where
i
v~
~
for every i {1, 2, ..., n}.
5. DYNAMICAL MODEL OF A CLOSED SERIAL QUEUING NETWORK WITH FUZZY ACTIVITIY TIME
