Summary
This paper performs a literature review of common
Multi-Criteria Decision Making methods, examines the advantages and
disadvantages of the identified methods, and explains how their common
applications relate to their relative strengths and weaknesses.
Multi-Attribute Utility Theory (MAUT)
MAUT is an expected utility theory that can decide the best
course of action in a given problem by assigning a utility to every possible
consequence and calculating the best possible utility. The major advantage of
MAUT is that it takes uncertainty into account. It is comprehensive and can
account for and incorporate the preferences of each consequence at every step
of the method. This amount of accuracy is convenient, however it can lead to
many possible disadvantages. An incredible amount of input is necessary at every
step of the procedure in order to accurately record the decision maker’s
preferences, making this method extremely data intensive. The preferences of
the decision makers also need to be precise, giving specific weights to each of
the consequences, which requires stronger assumptions at each level. MAUT has
seen heavy application in economic, financial, actuarial, water management,
energy management, and agricultural problems.
Analytic Hierarchy Process (AHP)
AHP is a theory of measurement through pairwise comparisons
and relies on the judgments of experts to derive priority scales. One of its
advantages is its ease of use. Its use of pairwise comparisons can allow
decision makers to weight coefficients and compare alternatives with relative
ease. It is scalable, and can easily adjust in size to accommodate decision
making problems due to its hierarchical structure. Besides, it is not nearly as
data intensive as MAUT. Due to the approach of pairwise comparisons, it can
also be subject to inconsistencies in judgment and ranking criteria. One of its
biggest criticisms is that the general form of AHP is susceptible to rank
reversal. Due to the nature of comparisons for rankings, the addition of
alternatives at the end of the process could cause the final rankings to flip
or reverse. AHP has seen much use in performance-type problems, resource
management, corporate policy and strategy, public policy, political strategy,
and planning
Fuzzy Theory
Fuzzy set theory is an extension of classical set theory
that allows solving a lot of problems related to dealing the imprecise and
uncertain data. It has many advantages. Fuzzy logic “takes into account the
insufficient information and the evolution of available knowledge. It allows
imprecise input. It allows a few rules to encompass problems with great
complexity. For disadvantages, fuzzy systems can sometimes be difficult to
develop. In many cases, they can require numerous simulations before being able
to be used in the real world. Fuzzy set theory is established and has been used
in applications such as engineering, economic, environmental, social, medical,
and management.
Case-Based Reasoning (CBR)
CBR is a MCDM method that retrieves cases similar to a
problem from an existing database of cases, and proposes a solution to a
decision-making problem based on the most similar cases. This provides the
first of its advantages, which is that it requires little effort in terms of
acquiring additional data. It also requires little maintenance as the database
will already be existing and requires little upkeep. One major advantage that
it has over most MCDM methods is that it can improve over time, especially as
more cases are added to the database. It can also adapt to changes in
environment with its database of cases. Its major drawback is its sensitivity
to inconsistency in data. Previous cases could be invalid or special cases may
results in invalid answers. Sometimes similar cases may not always be the most
accurate in terms of solving the problem at hand. CBR is used in industries
where a substantial number of previous cases already exist. This includes
comparisons of businesses, vehicle insurance, medicine, and engineering
designs.
Data Envelopment Analysis (DEA)
DEA uses a linear programming technique to measure the relative
efficiencies of alternatives. It rates the efficiencies of alternatives against
each other, with the most efficient alternative having a rating of 1.0, with
all other alternatives being a fraction of 1.0. It has a number of advantages.
It is capable of handling multiple inputs and outputs. Efficiency can be
analyzed and quantified. It can uncover relationships that may be in hidden
with other methods. An important disadvantage is that does not deal with imprecise
data and assumes that all input and output data are exactly known. In real
world situations, however, this assumption may not always be true. The results
can be sensitive depending on the inputs and outputs. DEA is used wherever
efficiencies need to be compared. This is commonly used in economic, medical,
utilities, road safety, agriculture, retail, and business problems.
Other Methods
SMART: SMART is one of the
simplest forms of MAUT. It requires two assumptions, namely utility
independence and preferential independence. This method conveniently converts
importance weights into actual numbers. Major advantages of SMART, in addition
to those described in MAUT, are that it is simple to use and it actually allows
for any type of weight assignment techniques. It requires less effort by
decision makers than MAUT. It also handles data well under each criterion. Like
MAUT, a disadvantage is that the procedure for determining work is not
convenient considering the complicated framework.
Goal Programming: Goal
Programming is a pragmatic programming method that is able to choose from an
infinite number of alternatives. One of its advantages is that it has the
capacity to handle large-scale problems. Its ability to produce infinite
alternatives provides a significant advantage over some methods, depending on
the situation. A major disadvantage is its inability to weight coefficients.
Many applications find it necessary to use other methods, such as AHP, to
properly weight the coefficients. Goal programming has seen applications in
production planning, scheduling, health care, portfolio selection, distribution
system design, energy planning, water reservoir management, timber harvest
scheduling, and wildlife management problems.
ELECTRE: ELECTRE, along with
its many iterations, is an outranking method based on concordance analysis. Its
major advantage is that it takes into account uncertainty and vagueness. One
disadvantage is that its process and outcomes can be hard to explain. Further,
due to the way preferences are incorporated, the lowest performances under
certain criteria are not displayed. The outranking method causes the strengths
and weaknesses of the alternatives to not be directly identified, nor results
and impacts to be verified. ELECTRE has been used in energy, economics,
environmental, water management, and transportation problems.
PROMETHEE: PROMETHEE is
similar to ELECTRE in that it also has several iterations and is also an
outranking method. Its advantage is that it is easy to use. It does not require
the assumption that the criteria are proportionate. The disadvantages are that
it does not provide a clear method by which to assign weights and it requires
the assignment of values but does not provide a clear method by which to assign
those values. PROMETHEE has seen much use in environmental management,
hydrology and water management, business and financial management, chemistry,
logistics and transportation, manufacturing and assembly, energy management,
and agriculture
SAW: SAW is a value function
is established based on a simple addition of scores that represent the goal
achievement under each criterion, multiplied by the particular weights. It has
the ability to compensate among criteria. It is also intuitive to decision
makers. The calculation is simple and can be performed without the help of
complex computer programs. It has specific disadvantages: All the values of the
criteria should be maximizing. Minimizing criteria should be transformed to
maximizing ones before being used in the analysis. All the values of the
criteria should be positive and the estimates yielded by SAW do not always
reflect the real situation. SAW has had applications in water management,
business, and financial management.
TOPSIS: TOPSIS is an
approach to identify an alternative which is closest to the ideal solution and
farthest to the negative ideal solution in a multi-dimensional computing space.
It has numerous advantages. It has a simple process. It is easy to use and
programmable. The number of steps remains the same regardless of the number of
attributes. A disadvantage is that its use of Euclidean Distance does not
consider the correlation of attributes. It is difficult to weight attributes
and keep consistency of judgment, especially with additional attributes. TOPSIS
has been used in supply chain management and logistics, design, engineering and
manufacturing systems, business and marketing management, environmental
management, human resources management, and water resources management.
Critique
This paper assesses the more common methods of MCDM in order
to benefit practitioners to choose a method for solving a specific problem.
Identification of common MCDM methods and identification of strengths and
weaknesses is very useful however; this article solely itself is not enough to
determine a method of MCDM. It only gives a general idea and helps researchers
during the method choosing process.
Source:
Posts
Having read through most of the techniques you have listed and others available on MCDM, I think the AHP is the most practical and easiest method for the intelligence community to employ because it involves the least amount of math. Likewise, the AHP is especially useful when dealing with qualitative elements like politics or regional disputes. In your opinion, which MCDM method or methods do you think is the overall best option in the intelligence community?
ReplyDeleteOleg, I agree with you. AHP is very common due to its ease of use. However, I think Fuzzy Theory can also be useful for the intelligence community because it allows solving a lot of problems related to dealing the imprecise and uncertain data and it takes into account the insufficient information and the evolution of available knowledge.
DeleteI think this article is useful in just getting a grasp on the range of varients that are out there. Most of the articles I had looked at utilized AHP, and it is good to at least have some information about what else is out there. The only question I would ask has already been posed by Oleg.
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