APPLYING TRANSFORMATION CHARACTERISTICS TO SOLVE THE MULTI OBJECTIVE LINEAR FRACTIONAL PROGRAMMING PROBLEMS

International Journal of Computer Science & Information Technology (IJCSIT) Vol 9, No 2, April 2017
DOI:10.5121/ijcsit.2017.9207 77
APPLYING TRANSFORMATION CHARACTERISTICS
TO SOLVE THE MULTI OBJECTIVE LINEAR
FRACTIONAL PROGRAMMING PROBLEMS
Wen Pei
Department of Business Administration, Chung Hua University, Hsinchu, Taiwan, ROC
ABSTRACT
For some management programming problems, multiple objectives to be optimized rather than a single
objective, and objectives can be expressed with ratio equations such as return/investment, operating
profit/net-sales, profit/manufacturing cost, etc. In this paper, we proposed the transformation
characteristics to solve the multi objective linear fractional programming (MOLFP) problems. If a MOLFP
problem with both the numerators and the denominators of the objectives are linear functions and some
technical linear restrictions are satisfied, then it is defined as a multi objective linear fractional
programming problem MOLFPP in this research. The transformation characteristics are illustrated and the
solution procedure and numerical example are presented.
KEYWORDS
Transformation Characteristics, MOLFP, MOLFPP
1. INTRODUCTION
Management programming problems are based upon estimated values. These problems have
multiple objectives to be optimized rather than a single objective. Thus optimal solution to one
objective function is not necessarily optimal for other objective functions and hence one need a
solution as the compromise solution. In the meantime, for some management programming
problems, objectives can be expressed in ratio equations such as return/investment, operating
profit/net-sales, profit/manufacturing cost, etc. These multiple objective fractional programming
models were first studied by Luhandjula [6] . Kornbluth and Steuer [4] have presented an
algorithm for solving the MOLFP by combining aspects of multiple objective , single objective
fractional programming and goal programming. Valipour et al. [9] suggested an iterative
parametric approach for solving MOLFP problems which only uses linear programming to obtain
efficient solutions and converges to a solution. Mishra et al. [7] presented a MOLFP approach
for multi objective linear fuzzy goal programming problem. Li et al. [5] proposed a two-level
linear fractional water management model based on interactive fuzzy programming. Saha et al.
[8] proposed an approach for solving linear fractional programing problem by converting it into a
single linear programming problem, which can be solved by using any type of linear fractional
programming technique.
Zimmermann [10,11] first applied fuzzy set theory concept with choices of membership
functions and derived a fuzzy linear program which is identical to the maximum program. He

78
showed that solutions obtained by fuzzy linear programming are efficient solutions and also gives
an optimal compromise solution. Luhandjula [6] solved MOLFP by applying fuzzy approach to
overcome the computational difficulties of using conventional fractional programming
approaches to solve multiple objective fractional programming problem. Charnes and Cooper [2]
have shown that a linear fractional programming problem can be optimized by reducing it to two
linear programs to solve MOLFP. Dutta et al [3] modified the linguistic approach of Luhandjula
[6] by constructing the desirable membership functions. Chakraborty and Gupta [1] proposed a
different methodology for solving MOLFP. The approach stated that suitable transformation
should have been applied to formulate an equivalent multi objective linear programming and the
resulting multi objective linear programming could be solved based on fuzzy set theoretic
approach.
In this research, based on Chakraborty and Gupta [1] , if a MOLFP problem with both the
numerators and the denominators of the objectives are linear functions and some technical linear
restrictions are satisfied, then it is defined as a MOLFPP. We propose the transformation
characteristics to solve the MOLFPP. The transformation characteristics are illustrated and the
solution procedure and numerical example presented.
2. METHODS
2.1. The Transformation Characteristics of MOLFPP
2.1.1. Fuzzy Linear Programming
Fuzzy linear programming is fuzzy set theory applied to linear multi criteria decision making
problems. The multi objective linear fractional programming problem can be considered as a
vector optimizing problem. The first step is to assign two values kU and kL as upper and lower
bounds for each objective function kZ :
kU = Highest acceptable level of achievement for objective k
kL = Aspired level of achievement for objective k
Let
kd = kk LU − = the degradation allowance for objective k .
Takes an element X that has a degree of membership in the k -th objective, denoted by a
membership function )(Xkµ , to transform the fuzzy model into a crisp single objective linear
programming model of λ . The range of the membership function is ]1,0[ .

79







≥
<<
−
−
−
≤
=
.0
,1
,1
)(
kk
kkk
kk
kk
kk
k
UZif
UZLif
LU
LZ
LZif
Xµ
This approach is similar, in many respects, to the weighted linear goal programming method.
2.1.2. Linear Fractional Programming
The general format of a classical linear fractional programming problem Charnes and Cooper [2]
can be stated as
Max
β
α
+
+
xd
xc
T
T
s.t.










∈≥










≥
=
≤
∈=∈ mn
RbxbAxRxXx ,0, , (1)
where n
Rdc ∈, ; R∈βα, , X is nonempty and bounded.
We proposed the basic transformation characteristic of the original objective to solve the problem.
The following transformation is proposed:
)(
)(
)(
)(
)(
)(
xN
xD
Min
xN
xD
Min
xD
xN
Max
xxx −
−
⇔⇔
∆∈∆∈∆∈
. (2)
2.1.3. Multiple Objectives Linear Fractional Programming Problem
The general format of maximizing MOLFPP can be written as
Max






























+
+
=
+
+
=
+
+
=
=
i
T
k
i
T
k
k
i
T
i
T
i
T
i
T
xd
xc
xZ
xd
xc
xZ
xd
xc
xZ
xZ
β
α
β
α
β
α
)(
)(
)(
)(
2
2
2
1
1
1
‧
‧
‧

80
s.t.










∈≥










≥
=
≤
∈=∈ mn
Where n
ii Rdc ∈, ; Rii ∈βα , , ki ,...2,1= , 2≥k , X is nonempty and bounded.
Similarly, minimum problem can also be defined as
Min )](),...,(),([)( 21 xZxZxZxZ k=
s.t.










∈≥










≥
=
≤
∈=∈ mn
Where n
ii Rdc ∈, ; Rii ∈βα , , ki ,...2,1= , 2≥k , X is nonempty and bounded,
with
)(
)(
)(
xD
xN
xd
xc
xZ
i
i
i
T
i
i
T
i
i =
+
+
=
β
α
.
The general format of minimum MOLFPP is as the following equivalent multi objective linear
programming problem:
Min tyc i
T
i α+ ,
s.t. γβ =+ tyd i
T
i
0≤− tbyA ii ,
y , 0≥t , ki ,...2,1= , 2≥k .
The membership functions for )(xNi and )(xDi are as followed:
If Ii ∈ , then
))(( tytNiiµ







≥
<<
−
−
≤
=
ii
ii
i
i
i
ZtytNfi
ZtytNif
Z
tytN
tytNif
)(1
)(0
0
0)(
0)(0
If c
Ii∈ , then
))(( tytDiiµ

81







≥
<<
−
−
≤
=
ii
ii
i
i
i
ZtytDfi
ZtytDif
Z
tytD
tytDif
)(1
)(0
0
0)(
0)(0
The Zimmermann’s [10,11] operator is used to transform the equivalent multi objective linear
programming problem into the crisp model as:
Max λ
s.t. λµ ≥))(( tytNii for Ii ∈ ,
λµ ≥))(( tytDii for c
Ii∈ ,
1)( ≤tytDi for Ii ∈ ,
1)( ≤− tytNi for c
Ii∈ ,
0)( ≤− btyA ,
0>t , 0≥y , ki ,...2,1= , 2≥k .
I is a set such that 0)(:{ ≥= xNiI i for some }∆∈x and 0)(:{ <= xNiI i
c
for each }∆∈x
where },...,2,1{ kII c
=U . The computing of iZ , is proceeded as “if Ii ∈ , then it may assume
the maximum aspiration level is *
ii ZZ = , and if c
Ii∈ , then *
1 ii ZZ −= .” The method
proposed in this paper suggests that with 0=t , by Charnes and Cooper [2] method, the problem
could not be solved, the transformation characteristics can be used to solve the MOLFPP.
3. RESULTS AND DISCUSSION
The solution procedure is stated and numerical examples adopted from Chakraborty and Gupta
[1] are used to show the transformation characteristics.
3.1. Solution Procedure
The transformation characteristics are used to solve MOLFPP when 0=t from the original
problems. The following procedure is developed:
Step 1. Solve the original MOLFPP by Charnes and Cooper [2] .
Step 2. If 0=t , the proposed methodology is applied.
Step 3. Solve the problem by Zimmermann’s [10,11] operator to transform the equivalent multi
objective linear programming problem into the crisp model.
3.2. Numerical Examples
Let’s consider a MOLFP with two objectives as follows:

82
Max












++
+
=
++
+−
=
=
125
7
)(
,
3
23
)(
)(
21
21
2
21
21
1
xx
xx
xZ
xx
xx
xZ
xZ
s.t. 121 ≥− xx ,
1532 21 ≤+ xx ,
31 ≥x ,
0≥ix , 2,1=i .
Solve the MOLFP by Charnes and Cooper [2] approach.
Max 





+=
+−=
)7),(
)23),(
212
211
yytyf
yytyf
s.t. 1321 ≤++ tyy ,
125 21 ≤++ tyy ,
021 ≥−− tyy ,
01532 21 ≤−− tyy ,
031 ≥− ty ,
iy , 0≥t , 2,1=i .
Where 01 =y , 02 =y , 0=t for ),(1 tyf , and 194805.01 =y , 02 =y , 025974.0=t
for ),(2 tyf . Thus )584415.0,0(),( 11 −=LU , )0,3636.1(),( 22 =LU
With Zimmerman’s [10,11] approach, the above multi objective linear programming problem
could be solved. The solution of the problem is obtained as 7741981.0=λ , 131962.01 =y ,
131962.02 =y , and 0=t . The original problem could be translated into the following
MOLFPP:
Min












+
++
=
+−
++
=
=
21
21
2
21
21
1
7
125
)(
,
23
3
)(
)(
xx
xx
xZ
xx
xx
xZ
xZ
s.t. 121 ≥− xx ,
1532 21 ≤+ xx ,
31 ≥x ,
0≥ix , 2,1=i .

83
The equivalent MOLFPP is as followed:
Min 





++=
−−−=
tyytyf
tyytyf
212
211
25),(
3),(
s.t. 123 21 ≤− yy ,
,
021 ≥−− tyy ,
01532 21 ≤−+ tyy ,
031 ≥− ty ,
iy , 2,1=i , 0≥t .
The solution are )3478.0,0(),( 11 −=LU , )0,8696.0(),( 22 =LU , 49998.0=λ ,
065215.01 =y , 043477.02 =y , and 021738.0=t . The solution of the original problem is:
31 =x , 22 =x ,
8
5
1
−
=Z ,
20
23
2 =Z .
4. CONCLUSIONS
The transformation characteristics to solve MOLFPP based on fuzzy set theoretic approach are
proposed in this research. The MOLFPP can be transformed into the equivalent appropriate multi
objective linear programming problem by using the transformation characteristics. The resulting
multi objective linear programming problem is solved using fuzzy set theoretic approach by
membership functions. Numerical example is utilized to illustrate the proposed methodology.
REFERENCE
[1] Chakraborty, M. and Gupta, S. (2002), “Fuzzy mathematical programming for multi objective linear
fractional programming problem,” Fuzzy Sets and Systems, Vol. 125, pp. 335-342.
[2] Charnes, A. and Cooper, W.W. (1962), “Programming with linear fractionals,” Naval Research
Logistics Quarterly, Vol. 9, pp. 181-186.
[3] Dutta, D., Tiwari, R.N. and Rao, J.R. (1992), “Multiple objective linear fractional programming－A
fuzzy set theoretic approach,” Fuzzy Sets and Systems, Vol. 52, pp. 39-45.
[4] Kornbluth, J.S.H. and Steuer, R.E. (1981), “Multiple objective linear fractional programming,”
Management Science, Vol. 27, pp. 1024-1039.
[5] Li, M., Guo, P., & Ren, C. (2015), “Water resources management models based on two-level linear
fractional programming method under uncertainty,” Journal of Water Resources Planning and
Management, 141(9), 05015001.
[6] Luhandjula, M.K. (1984), “Fuzzy approaches for multiple objective linear fractional optimization,”
Fuzzy Sets and Systems, Vol. 13, pp. 11-23.
[7] Mishra, B., Nishad, A. K., & Singh, S. R. (2014), “Fuzzy Multi-fractional Programming for Land Use
Planning in Agricultural Production System, ” Fuzzy Information and Engineering, 6(2), 245-262.
17 21 ≤+ yy

84
[8] Saha, S. K., Hossain, M. R., Uddin, M. K., & Mondal, R. N. (2015), “ A New Approach of Solving
Linear Fractional Programming Problem (LFP) by Using Computer Algorithm,” Open Journal of
Optimization, 4(03), 74.
[9] Valipour, E., Yaghoobi, M. A., & Mashinchi, M. (2014), “An iterative approach to solve multi
objective linear fractional programming problems,” Applied Mathematical Modelling, 38(1), 38-49.
[10] Zimmermann, H.J. (1976), “Description and optimization of fuzzy systems,” International Journal of
General Systems, Vol. 2, pp. 209-215.
[11] Zimmermann, H.J. (1978), “Fuzzy programming and linear programming with several objective
functions,” Fuzzy Sets and Systems, Vol. 1, pp. 45-55.

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