Search results for: model selection methods

Authors: Yoonsuh Jung

Abstract:

As DNA microarray data contain relatively small sample size compared to the number of genes, high dimensional models are often employed. In high dimensional models, the selection of tuning parameter (or, penalty parameter) is often one of the crucial parts of the modeling. Cross-validation is one of the most common methods for the tuning parameter selection, which selects a parameter value with the smallest cross-validated score. However, selecting a single value as an ‘optimal’ value for the parameter can be very unstable due to the sampling variation since the sample sizes of microarray data are often small. Our approach is to choose multiple candidates of tuning parameter first, then average the candidates with different weights depending on their performance. The additional step of estimating the weights and averaging the candidates rarely increase the computational cost, while it can considerably improve the traditional cross-validation. We show that the selected value from the suggested methods often lead to stable parameter selection as well as improved detection of significant genetic variables compared to the tradition cross-validation via real data and simulated data sets.

Keywords: Cross Validation, Parameter Averaging, Parameter Selection, Regularization Parameter Search.

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Authors: S. Sadi-Nezhad, K. Khalili Damghani, N. Pilevari

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In this article, a mathematical programming model for choosing an optimum portfolio of investments is developed. The investments are considered as investment projects. The uncertainties of the real world are associated through fuzzy concepts for coefficients of the proposed model (i. e. initial investment costs, profits, resource requirement, and total available budget). Model has been coded by using LINGO 11.0 solver. The results of a full analysis of optimistic and pessimistic derivative models are promising for selecting an optimum portfolio of projects in presence of uncertainty.

Keywords: Fuzzy Programming, Fuzzy Knapsack, FuzzyCapital Budgeting, Fuzzy Project Selection

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Authors: Päivi J. Tossavainen , Leena Alakoski, Katri Ojasalo

Abstract:

Building a service-centric business model requires new knowledge and capabilities in companies. This paper enlightens the challenges small and medium sized firms (SMEs) face when developing their service-centric business models. This paper examines the premise for knowledge transfer and capability development required. The objective of this paper is to increase knowledge about SME-s transformation to service-centric business models.This paper reports an action research based case study. The paper provides empirical evidence from three case companies. The empirical data was collected through multiple methods. The findings of the paper are: First, the developed model to analyze the current state in companies. Second, the process of building the service – centric business models. Third, the selection of suitable service development methods. The lack of a holistic understanding on service logic suggests that SMEs need practical and easy to use methods to improve their business

Keywords: service-centric business model, service development, action research, case study

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Authors: Safak Isik, Ozalp Vayvay

Abstract:

Strategic partnerships with suppliers play a vital role for the long-term value-based supply chain. This strategic collaboration keeps still being one of the top priority of many business organizations in order to create more additional value; benefiting mainly from supplier’s specialization, capacity and innovative power, securing supply and better managing costs and quality. However, many organizations encounter difficulties in initiating, developing and managing those partnerships and many attempts result in failures. One of the reasons for such failure is the incompatibility of members of this partnership or in other words wrong supplier selection which emphasize the significance of the selection process since it is the beginning stage. An effective selection process of strategic suppliers is critical to the success of the partnership. Although there are several research studies to select the suppliers in literature, only a few of them is related to strategic supplier selection for long-term partnership. The purpose of this study is to propose a conceptual model for the selection of strategic partnership suppliers. A two-stage approach has been used in proposed model incorporating first segmentation and second selection. In the first stage; considering the fact that not all suppliers are strategically equal and instead of a long list of potential suppliers, Kraljic’s purchasing portfolio matrix can be used for segmentation. This supplier segmentation is the process of categorizing suppliers based on a defined set of criteria in order to identify types of suppliers and determine potential suppliers for strategic partnership. In the second stage, from a pool of potential suppliers defined at first phase, a comprehensive evaluation and selection can be performed to finally define strategic suppliers considering various tangible and intangible criteria. Since a long-term relationship with strategic suppliers is anticipated, criteria should consider both current and future status of the supplier. Based on an extensive literature review; strategical, operational and organizational criteria have been determined and elaborated. The result of the selection can also be used to determine suppliers who are not ready for a partnership but to be developed for strategic partnership. Since the model is based on multiple criteria for both stages, it provides a framework for further utilization of Multi-Criteria Decision Making (MCDM) techniques. The model may also be applied to a wide range of industries and involve managerial features in business organizations.

Keywords: Kraljic’s matrix, purchasing portfolio, strategic supplier selection, supplier collaboration, supplier partnership, supplier segmentation.

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Authors: Ritsuko Kawasaki (Aiba), Takeshi Hiromatsu

Abstract:

Management is required to understand all information security risks within an organization, and to make decisions on which information security risks should be treated in what level by allocating how much amount of cost. However, such decision-making is not usually easy, because various measures for risk treatment must be selected with the suitable application levels. In addition, some measures may have objectives conflicting with each other. It also makes the selection difficult. Moreover, risks generally have trends and it also should be considered in risk treatment. Therefore, this paper provides the extension of the model proposed in the previous study. The original model supports the selection of measures by applying a combination of weighted average method and goal programming method for multi-objective analysis to find an optimal solution. The extended model includes the notion of weights to the risks, and the larger weight means the priority of the risk.

Keywords: Information security risk treatment, Selection of risk measures, Risk acceptanceand Multi-objective optimization.

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Authors: F. Jiménez, R. Jódar, M. Martín, G. Sánchez, G. Sciavicco

Abstract:

Abstract—Attribute or feature selection is one of the basic strategies to improve the performances of data classification tasks, and, at the same time, to reduce the complexity of classifiers, and it is a particularly fundamental one when the number of attributes is relatively high. Its application to unsupervised classification is restricted to a limited number of experiments in the literature. Evolutionary computation has already proven itself to be a very effective choice to consistently reduce the number of attributes towards a better classification rate and a simpler semantic interpretation of the inferred classifiers. We present a feature selection wrapper model composed by a multi-objective evolutionary algorithm, the clustering method Expectation-Maximization (EM), and the classifier C4.5 for the unsupervised classification of data extracted from a psychological test named BASC-II (Behavior Assessment System for Children - II ed.) with two objectives: Maximizing the likelihood of the clustering model and maximizing the accuracy of the obtained classifier. We present a methodology to integrate feature selection for unsupervised classification, model evaluation, decision making (to choose the most satisfactory model according to a a posteriori process in a multi-objective context), and testing. We compare the performance of the classifier obtained by the multi-objective evolutionary algorithms ENORA and NSGA-II, and the best solution is then validated by the psychologists that collected the data.

Keywords: Feature selection, multi-objective evolutionary computation, unsupervised classification, behavior assessment system for children.

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Authors: R.M.V. Silva, D.N. Souza

Abstract:

This paper presents a model for the characterization and selection of beeswaxes for use as base substitute tissue for the manufacture of objects suitable for external radiotherapy using megavoltage photon beams. The model of characterization was divided into three distinct stages: 1) verification of aspects related to the origin of the beeswax, the bee species, the flora in the vicinity of the beehives and procedures to detect adulterations; 2) evaluation of physical and chemical properties; and 3) evaluation of beam attenuation capacity. The chemical composition of the beeswax evaluated in this study was similar to other simulators commonly used in radiotherapy. The behavior of the mass attenuation coefficient in the radiotherapy energy range was comparable to other simulators. The proposed model is efficient and enables convenient assessment of the use of any particular beeswax as a base substitute tissue for radiotherapy.

Keywords: Beeswaxes, characterization, model, radiotherapy

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Authors: Bharatendra Rai

Abstract:

Predictive data analysis and modeling involving machine learning techniques become challenging in presence of too many explanatory variables or features. Presence of too many features in machine learning is known to not only cause algorithms to slow down, but they can also lead to decrease in model prediction accuracy. This study involves housing dataset with 79 quantitative and qualitative features that describe various aspects people consider while buying a new house. Boruta algorithm that supports feature selection using a wrapper approach build around random forest is used in this study. This feature selection process leads to 49 confirmed features which are then used for developing predictive random forest models. The study also explores five different data partitioning ratios and their impact on model accuracy are captured using coefficient of determination (r-square) and root mean square error (rsme).

Keywords: Housing data, feature selection, random forest, Boruta algorithm, root mean square error.

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Authors: R. Kozik, A. Leśniak A., E. Plebankiewicz

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Contracting authorities in the public sector are obligated to apply the principles provided for in the Polish law for the evaluation and selection of contractors. In order to analyse the methods of contractors selecting applied in practice by public clients, the notices of contract award results for construction works were analysed. The analysis shows that the procedure selected more and more often is open competitive bidding, where the assessment of the competence of contractors is not very precise, as well as noncompetitive bidding, i.e. single source procurement. The share of procurement procedures, where the only criterion is price, is increasing. The solution to the problems existing here might be the introduction of one of the forms of pre-selection of contractors. The article also briefly discusses verification systems for companies applying for public contracts used in EU countries.

Keywords: Certification, contractors selection, open tendering, public investors.

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Authors: Alexey Kulik

Abstract:

This article deals with selection standards for national sport teams. The author examines the legal framework for selection criteria and suggests using the most honest criteria.

Keywords: National teams, Standards of forming teams, Selection standards, Sport legislations.

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Authors: C. Ardil

Abstract:

This paper considers a comparative analysis of multiple criteria decision making analysis methods for strategic, tactical, and operational decisions in military fighter aircraft selection for the air force fleet planning. The evaluation criteria governing the decision analysis process are determined from the literature for the three existing military combat aircraft. Military fighter aircraft selection problem is structured using "preference analysis for reference ideal solution (PARIS)” approach in multiple criteria decision analysis (MCDMA). Systematic comparisons were made with existing MCDMA methods (PARIS, and TOPSIS) to verify the stability and accuracy of the results obtained. The proposed integrated MCDMA systematic approach is expected to address the issues encountered in the aircraft selection process. The comparative analysis results show that the proposed method is an effective and accurate tool that can help analysts make better strategic, tactical, and operational decisions.

Keywords: aircraft, military fighter aircraft selection, multiple criteria decision making, multiple criteria decision making analysis, mean weight, entropy weight, MCDMA, PARIS, TOPSIS, Saab Gripen, Dassault Rafale, Eurofighter Typhoon

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Authors: Jin Xu, Albert Y.S. Lam, Victor O.K. Li

Abstract:

Stock portfolio selection is a classic problem in finance, and it involves deciding how to allocate an institution-s or an individual-s wealth to a number of stocks, with certain investment objectives (return and risk). In this paper, we adopt the classical Markowitz mean-variance model and consider an additional common realistic constraint, namely, the cardinality constraint. Thus, stock portfolio optimization becomes a mixed-integer quadratic programming problem and it is difficult to be solved by exact optimization algorithms. Chemical Reaction Optimization (CRO), which mimics the molecular interactions in a chemical reaction process, is a population-based metaheuristic method. Two different types of CRO, named canonical CRO and Super Molecule-based CRO (S-CRO), are proposed to solve the stock portfolio selection problem. We test both canonical CRO and S-CRO on a benchmark and compare their performance under two criteria: Markowitz efficient frontier (Pareto frontier) and Sharpe ratio. Computational experiments suggest that S-CRO is promising in handling the stock portfolio optimization problem.

Keywords: Stock portfolio selection, Markowitz model, Chemical Reaction Optimization, Sharpe ratio

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Authors: Eric Jiang

Abstract:

As a popular rank-reduced vector space approach, Latent Semantic Indexing (LSI) has been used in information retrieval and other applications. In this paper, an LSI-based content vector model for text classification is presented, which constructs multiple augmented category LSI spaces and classifies text by their content. The model integrates the class discriminative information from the training data and is equipped with several pertinent feature selection and text classification algorithms. The proposed classifier has been applied to email classification and its experiments on a benchmark spam testing corpus (PU1) have shown that the approach represents a competitive alternative to other email classifiers based on the well-known SVM and naïve Bayes algorithms.

Keywords: Feature Selection, Latent Semantic Indexing, Text Classification, Vector Space Model.

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Authors: Amer M. Momani, Abdulaziz A. Ahmed

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The many feasible alternatives and conflicting objectives make equipment selection in materials handling a complicated task. This paper presents utilizing Monte Carlo (MC) simulation combined with the Analytic Hierarchy Process (AHP) to evaluate and select the most appropriate Material Handling Equipment (MHE). The proposed hybrid model was built on the base of material handling equation to identify main and sub criteria critical to MHE selection. The criteria illustrate the properties of the material to be moved, characteristics of the move, and the means by which the materials will be moved. The use of MC simulation beside the AHP is very powerful where it allows the decision maker to represent his/her possible preference judgments as random variables. This will reduce the uncertainty of single point judgment at conventional AHP, and provide more confidence in the decision problem results. A small business pharmaceutical company is used as an example to illustrate the development and application of the proposed model.

Keywords: Analytic Hierarchy Process (AHP), Materialhandling equipment selection, Monte Carlo simulation, Multi-criteriadecision making

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Authors: R. Ramli, M. I. Ghazali, H. Ibrahim, M. M. Kasim, F. M. Kamal, S.Vikneswari

Abstract:

Teachers form the backbone of any educational system, hence selecting qualified candidates is very crucial. In Malaysia, the decision making in the selection process involves a few stages: Initial filtering through academic achievement, taking entry examination and going through an interview session. The last stage is the most challenging since it highly depends on human judgment. Therefore, this study sought to identify the selection criteria for teacher candidates that form the basis for an efficient multi-criteria teacher-candidate selection model for that last stage. The relevant criteria were determined from the literature and also based on expert input that is those who were involved in interviewing teacher candidates from a public university offering the formal training program. There are three main competency criteria that were identified which are content of knowledge, communication skills and personality. Further, each main criterion was divided into a few subcriteria. The Analytical Hierarchy Process (AHP) technique was employed to allocate weights for the criteria and later, integrated a Simple Weighted Average (SWA) scoring approach to develop the selection model. Subsequently, a web-based Decision Support System was developed to assist in the process of selecting the qualified teacher candidates. The Teacher-Candidate Selection (TeCaS) system is able to assist the panel of interviewers during the selection process which involves a large amount of complex qualitative judgments.

Keywords: Analytic Hierarchy Process, Simple Weighted Average, Decision Support System, Multi-criteria decision making problem.

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Authors: K.-H. Yang

Abstract:

In the past, there was a lot of excellent research studies conducted on topics related to supplier selection. Because the considered factors of supplier selection are complicated and difficult to be quantified, most researchers deal supplier selection issues by qualitative approaches. Compared to qualitative approaches, quantitative approaches are less applicable in the real world. This study tried to apply the quantitative approach to study a supplier selection problem with considering operation cost and delivery reliability. By those factors, this study applies Normalized Normal Constraint Method to solve the dual objectives mixed integer program of the supplier selection problem.

Keywords: Bi-objectives MIP, normalized normal constraint method, supplier selection, quantitative approach.

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Authors: O. Starineca, I. Voronchuk

Abstract:

Human Resources (HR) selection and training have various implementation possibilities depending on an organization’s abilities and peculiarities. We propose to base HR selection and training decisions about on a competence-based approach. HR selection and training of employees are topical as there is room for improvement in this field; therefore, the aim of the research is to propose rational decision-making approaches for an organization HR selection and training choice. Our proposals are based on the training development and competence-based selection approaches created within previous researches i.e. Analytic-Hierarchy Process (AHP) and Linear Programming. Literature review on non-formal education, competence-based selection, AHP form our theoretical background. Some educational service providers in Latvia offer employees training, e.g. motivation, computer skills, accounting, law, ethics, stress management, etc. that are topical for Public Administration. Competence-based approach is a rational base for rational decision-making in both HR selection and considering HR training.

Keywords: Competence-based selection, human resource, training, decision-making.

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Authors: Mehtap Dursun, Zeynep Şener

Abstract:

Supplier selection is considered as one of the most critical issues encountered by operations and purchasing managers to sharpen the company’s competitive advantage. In this paper, a novel fuzzy multi-criteria group decision making approach integrating quality function deployment (QFD) and decision making trial and evaluation laboratory (DEMATEL) method is proposed for supplier selection. The proposed methodology enables to consider the impacts of inner dependence among supplier assessment criteria. A house of quality (HOQ) which translates purchased product features into supplier assessment criteria is built using the weights obtained by DEMATEL approach to determine the desired levels of supplier assessment criteria. Supplier alternatives are ranked by a distance-based method.

Keywords: DEMATEL, Group decision making, QFD, Supplier selection.

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Authors: Sahil, Rajeev Saha, Sanjeev Kumar

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Material selection is one of the key issues for the production of reliable and quality products in industries. A number of materials are available for a single product due to which material selection become a difficult task. The aim of this paper is to select appropriate material for gear used in fuel pump by using Graph Theory and Matrix Approach (GTMA). GTMA is a logical and systematic approach that can be used to model and analyze various engineering systems. In present work, four alternative material and their seven attributes are used to identify the best material for given product.

Keywords: Material, GTMA, MADM, digraph, decision making.

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Authors: Akrem Sellami, Imed Riadh Farah

Abstract:

Hyperspectral imagery (HSI) typically provides a wealth of information captured in a wide range of the electromagnetic spectrum for each pixel in the image. Hence, a pixel in HSI is a high-dimensional vector of intensities with a large spectral range and a high spectral resolution. Therefore, the semantic interpretation is a challenging task of HSI analysis. We focused in this paper on object classification as HSI semantic interpretation. However, HSI classification still faces some issues, among which are the following: The spatial variability of spectral signatures, the high number of spectral bands, and the high cost of true sample labeling. Therefore, the high number of spectral bands and the low number of training samples pose the problem of the curse of dimensionality. In order to resolve this problem, we propose to introduce the process of dimensionality reduction trying to improve the classification of HSI. The presented approach is a semi-supervised band selection method based on spatial hypergraph embedding model to represent higher order relationships with different weights of the spatial neighbors corresponding to the centroid of pixel. This semi-supervised band selection has been developed to select useful bands for object classification. The presented approach is evaluated on AVIRIS and ROSIS HSIs and compared to other dimensionality reduction methods. The experimental results demonstrate the efficacy of our approach compared to many existing dimensionality reduction methods for HSI classification.

Keywords: Hyperspectral image, spatial hypergraph, dimensionality reduction, semantic interpretation, band selection, feature extraction.

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Authors: Abeer Amayri, Akif A. Bulgak

Abstract:

Due to today’s globalization as well as outsourcing practices of the companies, the Supply Chain (SC) performances have become more dependent on the efficient movement of material among places that are geographically dispersed, where there is more chance for disruptions. One such disruption is the quality and delivery uncertainties of outsourcing. These uncertainties could lead the products to be unsafe and, as is the case in a number of recent examples, companies may have to end up in recalling their products. As a result of these problems, there is a need to develop a methodology for selecting suppliers globally in view of risks associated with low quality and late delivery. Accordingly, we developed a two-stage stochastic model that captures the risks associated with uncertainty in quality and delivery as well as a solution procedure for the model. The stochastic model developed simultaneously optimizes supplier selection and purchase quantities under price discounts over a time horizon. In particular, our target is the study of global organizations with multiple sites and multiple overseas suppliers, where the pricing is offered in suppliers’ local currencies. Our proposed methodology is applied to a case study for a US automotive company having two assembly plants and four potential global suppliers to illustrate how the proposed model works in practice.

Keywords: Global supply chains, quality, stochastic programming, supplier selection.

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Authors: Jihan Abdulazeez Ahmed, Adnan Mohsin Abdulazeez Brifcani

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This paper suggests a new internal architecture of holon based on feature selection model using the combination of Bees Algorithm (BA) and Artificial Neural Network (ANN). BA is used to generate features while ANN is used as a classifier to evaluate the produced features. Proposed system is applied on the Wine dataset, the statistical result proves that the proposed system is effective and has the ability to choose informative features with high accuracy.

Keywords: Artificial Neural Networks, Holonic Approach, Feature Selection, Bee Algorithm.

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Authors: K. A. Sumitra Devi, N. P. Banashree, Annamma Abraham

Abstract:

Partitioning is a critical area of VLSI CAD. In order to build complex digital logic circuits its often essential to sub-divide multi -million transistor design into manageable Pieces. This paper looks at the various partitioning techniques aspects of VLSI CAD, targeted at various applications. We proposed an evolutionary time-series model and a statistical glitch prediction system using a neural network with selection of global feature by making use of clustering method model, for partitioning a circuit. For evolutionary time-series model, we made use of genetic, memetic & neuro-memetic techniques. Our work focused in use of clustering methods - K-means & EM methodology. A comparative study is provided for all techniques to solve the problem of circuit partitioning pertaining to VLSI design. The performance of all approaches is compared using benchmark data provided by MCNC standard cell placement benchmark net lists. Analysis of the investigational results proved that the Neuro-memetic model achieves greater performance then other model in recognizing sub-circuits with minimum amount of interconnections between them.

Keywords: VLSI, circuit partitioning, memetic algorithm, genetic algorithm.

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Authors: C. Gunavathi, K. Premalatha

Abstract:

Tumor classification is a key area of research in the field of bioinformatics. Microarray technology is commonly used in the study of disease diagnosis using gene expression levels. The main drawback of gene expression data is that it contains thousands of genes and a very few samples. Feature selection methods are used to select the informative genes from the microarray. These methods considerably improve the classification accuracy. In the proposed method, Genetic Algorithm (GA) is used for effective feature selection. Informative genes are identified based on the T-Statistics, Signal-to-Noise Ratio (SNR) and F-Test values. The initial candidate solutions of GA are obtained from top-m informative genes. The classification accuracy of k-Nearest Neighbor (kNN) method is used as the fitness function for GA. In this work, kNN and Support Vector Machine (SVM) are used as the classifiers. The experimental results show that the proposed work is suitable for effective feature selection. With the help of the selected genes, GA-kNN method achieves 100% accuracy in 4 datasets and GA-SVM method achieves in 5 out of 10 datasets. The GA with kNN and SVM methods are demonstrated to be an accurate method for microarray based tumor classification.

Keywords: F-Test, Gene Expression, Genetic Algorithm, k- Nearest-Neighbor, Microarray, Signal-to-Noise Ratio, Support Vector Machine, T-statistics, Tumor Classification.

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Authors: Teklu Urgessa, Wookjae Maeng, Joong Seek Lee

Abstract:

Application of five implementations of three data mining classification techniques was experimented for extracting important insights from tourism data. The aim was to find out the best performing algorithm among the compared ones for tourism knowledge discovery. Knowledge discovery process from data was used as a process model. 10-fold cross validation method is used for testing purpose. Various data preprocessing activities were performed to get the final dataset for model building. Classification models of the selected algorithms were built with different scenarios on the preprocessed dataset. The outperformed algorithm tourism dataset was Random Forest (76%) before applying information gain based attribute selection and J48 (C4.5) (75%) after selection of top relevant attributes to the class (target) attribute. In terms of time for model building, attribute selection improves the efficiency of all algorithms. Artificial Neural Network (multilayer perceptron) showed the highest improvement (90%). The rules extracted from the decision tree model are presented, which showed intricate, non-trivial knowledge/insight that would otherwise not be discovered by simple statistical analysis with mediocre accuracy of the machine using classification algorithms.

Keywords: Classification algorithms; data mining; tourism; knowledge discovery.

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Authors: Watcharin Sangma, Onsiri Chanmuang, Pitsanu Tongkhow

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 A forecasting model for steel demand uncertainty in Thailand is proposed. It consists of trend, autocorrelation, and outliers in a hierarchical Bayesian frame work. The proposed model uses a cumulative Weibull distribution function, latent first-order autocorrelation, and binary selection, to account for trend, time-varying autocorrelation, and outliers, respectively. The Gibbs sampling Markov Chain Monte Carlo (MCMC) is used for parameter estimation. The proposed model is applied to steel demand index data in Thailand. The root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) criteria are used for model comparison. The study reveals that the proposed model is more appropriate than the exponential smoothing method.

Keywords: Forecasting model, Steel demand uncertainty, Hierarchical Bayesian framework, Exponential smoothing method.

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Authors: Wanli Ma, Dat Tran, Dharmendra Sharma

Abstract:

The temporal nature of negative selection is an under exploited area. In a negative selection system, newly generated antibodies go through a maturing phase, and the survivors of the phase then wait to be activated by the incoming antigens after certain number of matches. These without having enough matches will age and die, while these with enough matches (i.e., being activated) will become active detectors. A currently active detector may also age and die if it cannot find any match in a pre-defined (lengthy) period of time. Therefore, what matters in a negative selection system is the dynamics of the involved parties in the current time window, not the whole time duration, which may be up to eternity. This property has the potential to define the uniqueness of negative selection in comparison with the other approaches. On the other hand, a negative selection system is only trained with “normal" data samples. It has to learn and discover unknown “abnormal" data patterns on the fly by itself. Consequently, it is more appreciate to utilize negation selection as a system for pattern discovery and recognition rather than just pattern recognition. In this paper, we study the potential of using negative selection in discovering unknown temporal patterns.

Keywords: Artificial Immune Systems, ComputationalIntelligence, Negative Selection, Pattern Discovery.

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Authors: C. Ardil

Abstract:

Fuzzy set and intuitionistic fuzzy set are dealing with the imprecision and uncertainty inherent in a complex decision problem. However, sometimes these theories are not sufficient to model indeterminate and inconsistent information encountered in real-life problems. To overcome this insufficiency, the neutrosophic set, which is useful in practical applications, is proposed, triangular neutrosophic numbers and trapezoidal neutrosophic numbers are examined, their definitions and applications are discussed. In this study, a decision making algorithm is developed using neutrosophic set processes and an application is given in fighter aircraft selection as an example of a decision making problem. The estimation of the fighter aircraft selection with the neutrosophic multiple criteria decision analysis method is examined.  

Keywords: neutrosophic set, multiple criteria decision making analysis, fighter aircraft selection, MCDMA, neutrosophic numbers

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Authors: C. Ardil

Abstract:

In this paper, composite programming is discussed for aircraft evaluation and selection problem using the multiple criteria decision analysis method. The decision criteria and aircraft alternatives were identified from the literature review. The importance of criteria weights was determined by the standard deviation method. The proposed model is applied to a practical decision problem for evaluating and selecting advanced jet trainer and light attack aircraft. The proposed technique gives robust and efficient results in modeling multiple criteria decisions. As a result of composite programming analysis, Hürjet, an advanced jet trainer and light attack aircraft alternative (a3), was chosen as the most suitable aircraft candidate.  

Keywords: composite programming, additive weighted model, multiplicative weighted model, multiple criteria decision making analysis, MCDMA, aircraft selection, advanced jet trainer and light attack aircraft, M-346, FA-50, Hürjet

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Authors: C. Ardil

Abstract:

This paper proposes entropic programming for the freighter aircraft selection problem using the multiple criteria decision analysis method. The study aims to propose a systematic and comprehensive framework by focusing on the perspective of freighter aircraft selection. In order to achieve this goal, an integrated entropic programming approach was proposed to evaluate and rank alternatives. The decision criteria and aircraft alternatives were identified from the research data analysis. The objective criteria weights were determined by the mean weight method and the standard deviation method. The proposed entropic programming model was applied to a practical decision problem for evaluating and selecting freighter aircraft. The proposed entropic programming technique gives robust, reliable, and efficient results in modeling decision making analysis problems. As a result of entropic programming analysis, Boeing B747-8F, a freighter aircraft alternative ( a3), was chosen as the most suitable freighter aircraft candidate.   

Keywords: entropic programming, additive weighted model, multiple criteria decision making analysis, MCDMA, TOPSIS, aircraft selection, freighter aircraft, Boeing B747-8F, Boeing B777F, Airbus A350F

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