Using different electre methods in strategic planning in the presence of human behavioral resistance



Download 297,89 Kb.
View original pdf
Page1/3
Date17.09.2019
Size297,89 Kb.
  1   2   3

Using different ELECTRE methods in strategic planning in
the presence of human behavioral resistance
Citation
Milani, AS, A. Shanian, and C. El-Lahham. Using Different
ELECTRE Methods in Strategic Planning in the Presence of
Human Behavioral Resistance Journal of Applied Mathematics and Decision Sciences 2006 (2006): 1–19.
As Published
http://dx.doi.org/10.1155/JAMDS/2006/10936
Publisher
Hindawi Publishing Corporation
Version
Final published version
Accessed
Wed Jul 18 18:55:01 EDT 2018
Citable Link
http://hdl.handle.net/1721.1/96127
Terms of Use
Creative Commons Attribution
Detailed Terms
http://creativecommons.org/licenses/by/2.0
The MIT Faculty has made this article openly available.
Please share
how this access benefits you. Your story matters.

USING DIFFERENT ELECTRE METHODS IN
STRATEGIC PLANNING IN THE PRESENCE
OF HUMAN BEHAVIORAL RESISTANCE
A. S. MILANI, A. SHANIAN, AND C. EL-LAHHAM
Received 16 December 2005; Revised 26 June 2006; Accepted 24 July 2006
In the multicriteria strategic planning of an organization, management should often be aware of employees resistance to change before making new decisions otherwise, a chosen strategy, though technologically acceptable, may not bee cient in the long term. This paper, using a sample case study within an organization, shows how di
fferent versions of
ELECTRE methods can be used in choosing e
fficient strategies that account for both human behavioral resistance and technical elements. Thee ect of resistance from each subsystem of the organization is studied to ensure the reliability of the chosen strategy.
The comparison of results from a select number of compensatory and noncompensatory models (ELECTRE I, III, IV, IS TOPSIS; SAW MaxMin) suggests that when employee resistance is a decision factor in the multicriteria strategic planning problem, the models can yield low-resistance strategies however, ELECTRE seems to show more reasonable sensitivity.
Copyright © 2006 AS. Milani et al. This is an open access article distributed under the
Creative Commons Attribution License, which permits unrestricted use, distribution,
and reproduction in any medium, provided the original work is properly cited.
1. Introduction
Strategic planning in general aims at improving the competence of an organization regarding, for example, its technical abilities, management, and employee culture. One important yardstick for choosing e
fficient strategies, however, is human behavioral resistance within the organization. Human behavioral resistance is a natural response to a change because a change normally involves going from known to unknown [
3
]. There area number of case studies (e.g., [
19
]) that show that employee resistance is the most frequent type of resistance encountered by managers during organizational changes. Example of such changes maybe the restructuring and realigning of departments or divisions,
major reorganization of systems and procedures, and the introduction of innovative process technologies [
2
]. If management focuses only on the technical elements of these
Hindawi Publishing Corporation
Journal of Applied Mathematics and Decision Sciences
Volume 2006, Article ID 10936, Pages
1

19
DOI 10.1155/JAMDS/2006/10936

2
ELECTRE strategic planning and organizational change changes, without taking into account the equally important human behavioral resistance element, it can crucially undermine the organizational e
fficiency On the other hand, since each individual and his/her perception of resistance is normally di
fferent from others, it would be desirable to combine individual behavioral resistances into an overall factor representing the resistance of a team, department, and eventually the whole organization. It is shown that overall indices in multiple criteria optimization methods can be employed to this end [
9
]. Once an overall resistance for di
fferent strategies is defined, it can be included as anew criterion (strategy resistance factor) next to other technical criteria in a decision matrix. Subsequently, choosing the proper strategy is reduced to solving a conventional discrete optimization problem using available multiple attribute decision-making (MADM) models.
There are essentially two di
fferent approaches for solving MADM problems compensatory and noncompensatory. The main di
fference between the two is that in compensatory models explicit tradeo
ffs among attributes can be permitted. Compensatory
MADM models have been based mainly on the multiattribute utility theory (MAUT) where a single overall criterion is estimated and optimized, and therefore direct compensations between attributes maybe allowed. Commonly used examples of MAUT or
MAUT-related methods are SAW (simple additive weighting method) [
8
] and AHP (analytic hierarchy process) [
16
]. The noncompensatory MADM models are mainly based on comparisons of alternatives, which are made with respect to individual criteria. An example of the latter approach is the ELECTRE (ELimination Et Choix Traduisant la
REalit´e) method [
10
]. While it is argued (e.g., [
1
]) that ELECTRE is principally noncom- pensatory, a few references (e.g., [
6
]) consider it as a partially compensatory submodel.
What distinguishes ELECTRE from compensatory models such as SAW is the fact that the weights in ELECTRE are “coe
fficients of importance (not criteria substitution rates)
and, moreover, a very bad value on a criterion cannot be o
ffset by good values on other criteria Nevertheless, in many strategic planning problems, there is no precise measure to select a correct model [
7
]. Fora given problem, there are both compatibilities and incompatibilities using each model. In such situations, it is reasonable to examine di
fferent models, which normally yield di
fferent solutions, before making a final decision Each method emphasizes di
fferent aspects of the decision and a good choice can then be made among the alternatives suggested by the majority of the methods.
In [
9
], the TOPSIS (technique of ranking preferences by similarity to the ideal solution) compensatory method [
6
] is discussed and employed to solve a multicriteria strategic planning problem of a local gas company in the presence of its complex employee resistance structure. This paper, using di
fferent versions of the ELECTRE methods, examines the outranking approach to solve the same problem. The results are compared to
TOPSIS as well as to a fully compensatory (SAW) and a fully noncompensatory method
(MaxMin) to verify thee ect of compensations and noncompensations in the methods and their sensitivity to the human resistance factor. It is of particular interest to see how di
fferent approaches of the MADM models differ from each other when employee resistance is a critical factor in the problem. Thee ect of individual subsystem resistances in the organization is also studied to ensure the reliability of the chosen strategy by
ELECTRE.
AS. Milani et al.
3
The rest of this article is organized as follows.
Section briefly reviews principles of the ELECTRE methods as well as their similarities and di
fferences. A comparison of the outranking approach in ELECTRE with the one used in the MUAT or MAUT-like models is also presented in this section.
Section gives a summary of the proposed sample case study.
Section uses a select number of ELECTRE methods to solve the strategic planning problem of an organization. In this section, results are discussed and compared to di
fferent MADM approaches. In
Section 5
, a comparative sensitivity analysis is carried outwith respect to the human behavioral resistance factor. Finally, the concluding remarks are presented in
Section 6
2. Principles of ELECTRE methods
The original ELECTRE approach has first appeared in [
10
]. Atypical MADM problem that the method aims to solve consists of
(i)
m alternatives M
i
,
i
=
1,
. . . , m;
(ii)
n criteria g
j
,
j
=
1,
. . . , n;
(iii)
n weighting factors ω
j
,
j
=
1,
. . . , n, normally,

n
j
=
1
ω
j
=
1.
The goal is then to select the best alternative given the performance values of each alternative with respect to each criterion (given as an
m
×
n decision matrix) and the corresponding weights of the criteria. For modeling the preference information between each pair of alternatives, such as
M
i
and
M
k
(
i, k
=
1,
. . . , m), ELECTRE uses the concept of outranking relations. A true outranking relation of
M
i

M
k
(also denoted as
M
i
SM
k
)
implies that
M
i
is preferred to
M
k
if
M
i
is at least as good as
M
k
on a majority of criteria and if it is not significantly bad on any other criteria (i.e., the di
fference between the two are within a predefined threshold) [
4
]. By establishing such a relation between each and every pair of alternatives, one can then eliminate the dominated alternatives and arrive at the nondominated solutions. The construction of the outranking relations, however,
is not an unambiguous task, particularly in the presence of conflicting multiple criteria.
Furthermore, there are cases where the given values in the decision matrix are not crisp
(e.g., due to uncertainty in the data).
The identification of an outranking relation between
M
i
and
M
k
requires two sets of comparisons one among the criteria in which
g
j
(
M
i
) is superior to
g
j
(
M
k
), one among the criteria in which
g
j
(
M
i
) is not superior to
g
j
(
M
k
). In other words, the ELECTRE
methods need to separately examine both the criteria that vote for
M
i

M
k
and those that veto such relation. These two sets of comparisons are performed based on the so- called concordance and discordance tests.
The concordance test allows the decision maker (DM) to verify if
M
i
is at least as good as
M
k
. In some of the ELECTRE methods (e.g., ELECTRE I and II [
13
]), such a testis binary in nature the concordance index is 1 when the testis passed and it is 0 when the testis failed. For example, if the criterion
g
j
is to be maximized, the condition
g
j
(
M
i
)
<
g
j
(
M
k
) results in a failed concordance test, whereas the condition
g
j
(
M
i
)

g
j
(
M
k
) results in a passed test. Some other ELECTRE methods (e.g., ELECTRE III [
11
], IV [
5
], and IS) use a fuzzy outranking relation and pseudocriteria (described in
Section 2.2
), where the concordance index can take values between 0 and 1, depending on how far
g
j
(
M
i
) is better than
g
j
(
M
k
).

4
ELECTRE strategic planning and organizational change
The other extremity of the concordance testis the discordance test. It checks if there exists a very high opposition to the outranking relation
M
i
SM
k
. This testis intended for the criteria in which
M
i
performs worse than
M
k
, and it can be binary or fuzzy. If the test fails, it can be said that there is a high opposition vetoing the concordance test. For instance, if an alternative has the best values regarding some criteria but at the same time it has significantly low values regarding some other criteria, it is likely that it passes the concordance test but not the discordance test [
4
]. Only when both the concordance and discordance tests are passed, it can then be said that the outranking relation of
M
i
SM
k
is true. If neither
M
i
SM
k
nor
M
k
SM
i
, then
M
i
RM
k
, meaning that
M
i
is incomparable to
M
k
When
M
i
is indi
fferent to M
k
, it is said
M
i
IM
k
, implying that one is not preferred over another for the DM.
In the following a brief description of solution procedures in di
fferent ELECTRE methods is presented. Detailed operations of each method can be found in references such as. Here the goal is to point out the main similarities and di
ffer- ences in the solution mechanisms. Generally speaking, ELECTRE I and IS are designed for selection problems, whereas ELECTRE II, III, and IV are used for ranking problems. Regardless, it is intended to examine how the di
fferent solution mechanisms of each method would a
ffect the top rank solutions (here strategies) in the presence of human behavioral resistance in a strategic planning problem. This is part of the motivation for the case study presented in
Section 3
2.1. ELECTRE I. A concordance and discordance index set is first defined for each and every pair of alternatives
M
i
and
M
k
,
i, k
=
1,
. . . , m, i
=
k (note that an alternative is not compared to itself):
concordance index set
=
J
+
ik
=

j
|
r
i j

r
k j

,
discordance index set
=
J

ik
=

j
|
r
i j
< r
k j

,
(2.1)
r
i j
refers to a component of the decision matrix with the
ith alternative and the jth criterion. Second, for each pair, the DM’s weights for the corresponding concordance set are summed to arrive at a global concordance index,
C
ik
(0

C
ik

1),
C
ik
=

j

J
+
ik
ω
j

n
j
=
1
ω
j
.
(2.2)
Similarly, a global discordance index for each pair of alternatives is defined,
D
ik
(0

D
ik

1),
D
ik
=
max
j

J

ik

ω
j

r
i j

r
k j

max
j
∈{
1,
...,n
}

ω
j

r
i j

r
k j

.
(2.3)
Next, a global concordance threshold,
c, and a global discordance threshold, d, are chosen to perform the global concordance and discordance tests. The more severe the threshold values, the more di
fficult it is to pass the tests (normally, c
=
0
.7 and d
=
0
.3 [
4
]). For an outranking relation to be judged as true, both global indices should not violate their corresponding thresholds. That is,
C
ik

c and D
ik

d. Once the two tests are completed
AS. Milani et al.
5
for all pairs of alternatives, the preferred alternatives are those that outrank more than being outranked.
Remark 2.1. To facilitate the mathematical implementation of the method, in some references such as [
6
], a concordance and discordance Boolean matrix is used to convert the results of each global concordance and discordance test to zero and one. The alternatives are then ranked using a final concordance-discordance Boolean matrix, which is found by an element-to-element product of the concordance and discordance matrices.
2.1.1. A modified version of ELECTRE Ii van Delft and Nijkamp [
18
] suggested that to avoid defining the threshold values in ELECTRE I, one may define for each alternative a net concordance index (
C
i
=

m
k
=
1,
k
=
i
(
C
ik

C
ki
),
i
=
1,
. . . , m) and/or a net discordance index (
D
i
=

m
k
=
1,
k
=
i
(
D
ik

D
ki
),
i
=
1,
. . . , m). The net concordance and discordance indices provide the DM with an e
ffective numerical measure to sort all the alternatives from the best to the worst. Higher net concordance and lower net discordance values are always preferred.
2.2. ELECTRE IS. This method is quite similar to ELECTRE I except that pseudocriteria instead of true criteria are used. For the
j criterion, the pseudocriterion is a function in which the discrimination between two alternatives is characterized by two thresholds the indi
fference threshold q
j
and the strict preference threshold
p
j
(
p
j

q
j
). The indi
fference threshold may represent the minimum boundary of uncertainty in the given data, while the strict preference threshold may represent the maximum boundary of uncertainty.
In comparing each two alternatives such as
M
i
and
M
k
with respect to the criterion
g
j
,
depending on the di
fference between the two alternative performances (i.e., g
j
(
M
k
)

g
j
(
M
i
)), the concordance index,
c
j
(
M
i
,
M
k
), can take a value between 0 and 1. Assuming that
g
j
is to be maximized, the concordance index can be given by
c
j

M
i
,
M
k

=













0
p
j
< g
j

M
k


g
j

M
i

,
g

M
i

+
p
j

g

M
k

p
j

q
j
q
j
< g
j

M
k


g
j

M
i


p
j
,
1
g
j

M
k


g
j

M
i


q
j
.
(2.4)
Then, similar to ELECTRE I, the concordance index values are aggregated in a global concordance index using the DM’s weights:
C
ik
=

n
j
=
1
ω
j
c
j
(
M
i
,
M
k
)
/

n
j
=
1
ω
j
. The discordance indices remain binary (0 or 1). The main advantage of the method is that it allows the DM to choose the decision parameters as intervals instead of fixed (true) values. It is worth noting that when
p
j
=
q
j
, a pseudocriterion becomes a true criterion.
2.3. ELECTRE II. The main di
fference between this method and ELECTRE I lies in defining two outranking relations instead of one the strong outranking and the weak outranking.
M
i
strongly outranks
M
k
if the corresponding concordance test for examining iMiiiiiSMiikiis passed strongly and the discordance testis passed fairly, or when the concordance testis passed fairly and the discordance testis passed strongly. On the other hand,
M
i
weakly outranks
M
k
if the concordance testis passed weakly and the discordance

6
ELECTRE strategic planning and organizational change testis passed fairly. Note that in this method, all criteria are true. The problem with this method is that it requires too many threshold parameters in order to define the above two types of outranking relations. Namely, three global concordance thresholds (two for strong outranking and one for weak outranking) and two discordance thresholds for each criterion should be defined [
4
]. The implementation of the method is rather complex but it does provide a powerful process for the final classification of alternatives based on the obtained outranking relations.
2.4. ELECTRE III. While this method uses the same principles of ELCTRE II, it is similar to ELECTRE IS in that it uses pseudocriteria instead of classical true criteria. More precisely, for each criterion, an indi
fference threshold and a strict preference threshold are defined, each of which can be a constant or a function of the corresponding criterion value. Using these thresholds, the fuzzy presentations of the outranking relations are then possible. The main di
fference between this method and ELECTRE IS is in the fact that here both concordance and discordance indices are fuzzy (note that in ELECTRE IS
the discordance index was binary. The discordance index in its fuzzy form is given as follows:
d
j

M
i
,
M
k

=













0
g
j

M
k


g
j

M
i

< p
j
,
g

M
k


p
j

g

M
i

v
j

p
j
p
j

g
j

M
k


g
j

M
i


v
j
,
1
v
j
< g
j

M
k


g
j

M
i

,
(2.5)
where
v
j
is called the veto threshold with respect to the
jth criterion (v
j

p
j

q
j
). Furthermore, instead of defining a global discordance index, in ELECTRE III an outranking credibility degree is defined by combining the discordance indices and the global concordance index with respect to a set of criteria for which the discordance index values are greater than the global concordance index value. Finally, similar to ELECTRE II, the classification of alternatives is performed using the obtained credibility degrees and a fuzzy outranking relation on ascending and descending distillation processes [
4
].


Share with your friends:
  1   2   3


The database is protected by copyright ©sckool.org 2019
send message

    Main page