Abstract: In this paper, multiple criteria decision making analysis technique, is presented for ranking and selection of a set of determined alternatives - fighter aircraft - which are associated with a set of decision factors. In fighter aircraft design, conflicting decision criteria, disciplines, and technologies are always involved in the design process. Multiple criteria decision making analysis techniques can be helpful to effectively deal with such situations and make wise design decisions. Multiple criteria decision making analysis theory is a systematic mathematical approach for dealing with problems which contain uncertainties in decision making. The feasibility and contributions of applying the multiple criteria decision making analysis technique in fighter aircraft selection analysis is explored. In this study, an integrated framework incorporating multiple criteria decision making analysis technique in fighter aircraft analysis is established using entropy objective weighting method. An improved integrated multiple criteria decision making analysis method is utilized to aggregate the multiple decision criteria into one composite figure of merit, which serves as an objective function in the decision process. Therefore, it is demonstrated that the suitable multiple criteria decision making analysis method with decision solution provides an effective objective function for the decision making analysis. Considering that the inherent uncertainties and the weighting factors have crucial decision impacts on the fighter aircraft evaluation, seven fighter aircraft models for the multiple design criteria in terms of the weighting factors are constructed. The proposed multiple criteria decision making analysis model is based on integrated entropy index procedure, and additive multiple criteria decision making analysis theory. Hence, the applicability of proposed technique for fighter aircraft selection problem is considered. The constructed multiple criteria decision making analysis model can provide efficient decision analysis approach for uncertainty assessment of the decision problem. Consequently, the fighter aircraft alternatives are ranked based their final evaluation scores, and sensitivity analysis is conducted.
Abstract: The shortest path routing problem is a multiobjective nonlinear optimization problem with constraints. This problem has been addressed by considering Quality of service parameters, delay and cost objectives separately or as a weighted sum of both objectives. Multiobjective evolutionary algorithms can find multiple pareto-optimal solutions in one single run and this ability makes them attractive for solving problems with multiple and conflicting objectives. This paper uses an elitist multiobjective evolutionary algorithm based on the Non-dominated Sorting Genetic Algorithm (NSGA), for solving the dynamic shortest path routing problem in computer networks. A priority-based encoding scheme is proposed for population initialization. Elitism ensures that the best solution does not deteriorate in the next generations. Results for a sample test network have been presented to demonstrate the capabilities of the proposed approach to generate well-distributed pareto-optimal solutions of dynamic routing problem in one single run. The results obtained by NSGA are compared with single objective weighting factor method for which Genetic Algorithm (GA) was applied.
Abstract: Disposal of health-care waste (HCW) is considered as
an important environmental problem especially in large cities.
Multiple criteria decision making (MCDM) techniques are apt to deal
with quantitative and qualitative considerations of the health-care
waste management (HCWM) problems. This research proposes a
fuzzy multi-criteria group decision making approach with a multilevel
hierarchical structure including qualitative as well as
quantitative performance attributes for evaluating HCW disposal
alternatives for Istanbul. Using the entropy weighting method,
objective weights as well as subjective weights are taken into account
to determine the importance weighting of quantitative performance
attributes. The results obtained using the proposed methodology are
thoroughly analyzed.
Abstract: The design of weight is one of the important parts in
fuzzy decision making, as it would have a deep effect on the evaluation
results. Entropy is one of the weight measure based on objective
evaluation. Non--probabilistic-type entropy measures for fuzzy set
and interval type-2 fuzzy sets (IT2FS) have been developed and applied
to weight measure. Since the entropy for (IT2FS) for decision
making yet to be explored, this paper proposes a new objective
weight method by using entropy weight method for multiple attribute
decision making (MADM). This paper utilizes the nature of IT2FS
concept in the evaluation process to assess the attribute weight based
on the credibility of data. An example was presented to demonstrate
the feasibility of the new method in decision making. The entropy
measure of interval type-2 fuzzy sets yield flexible judgment and
could be applied in decision making environment.
Abstract: The shortest path routing problem is a multiobjective
nonlinear optimization problem with constraints. This problem has
been addressed by considering Quality of service parameters, delay
and cost objectives separately or as a weighted sum of both
objectives. Multiobjective evolutionary algorithms can find multiple
pareto-optimal solutions in one single run and this ability makes them
attractive for solving problems with multiple and conflicting
objectives. This paper uses an elitist multiobjective evolutionary
algorithm based on the Non-dominated Sorting Genetic Algorithm
(NSGA), for solving the dynamic shortest path routing problem in
computer networks. A priority-based encoding scheme is proposed
for population initialization. Elitism ensures that the best solution
does not deteriorate in the next generations. Results for a sample test
network have been presented to demonstrate the capabilities of the
proposed approach to generate well-distributed pareto-optimal
solutions of dynamic routing problem in one single run. The results
obtained by NSGA are compared with single objective weighting
factor method for which Genetic Algorithm (GA) was applied.
Abstract: In the multi objective optimization, in the case when generated set of Pareto optimal solutions is large, occurs the problem to select of the best solution from this set. In this paper, is suggested a method to order of Pareto set. Ordering the Pareto optimal set carried out in conformity with the introduced distance function between each solution and selected reference point, where the reference point may be adjusted to represent the preferences of a decision making agent. Preference information about objective weights from a decision maker may be expressed imprecisely. The developed elicitation procedure provides an opportunity to obtain surrogate numerical weights for the objectives, and thus, to manage impreciseness of preference. The proposed method is a scalable to many objectives and can be used independently or as complementary to the various visualization techniques in the multidimensional case.