A Problem-based Selection of Multi-attribute Decision-making Methods

Different multi-attribute decision-making (MADM) methods often produce different outcomes for selecting or ranking a set of decision alternatives involving multiple attributes. This paper presents a new approach to the selection of compensatory MADM methods for a specific cardinal ranking problem via sensitivity analysis of attribute weights. In line with the context-dependent concept of informational importance, the approach examines the consistency degree between the relative degree of sensitivity of individual attributes using an MADM method and the relative degree of influence of the corresponding attributes indicated by Shannon's entropy concept. The approach favors the method that has the highest consistency degree as it best reflects the decision information embedded in the problem data set. An empirical study of a scholarship student selection problem is used to illustrate how the approach can validate the ranking outcome produced by different MADM methods. The empirical study shows that different problem data sets may result in a different method being selected. This approach is particularly applicable to large-scale cardinal ranking problems where the ranking outcome of different methods differs significantly.     

Multiple-attribute decision making methods for plant layout design problem

The layout design problem is a strategic issue and has a significant impact on the efficiency of a manufacturing system. Much of the existing layout design literature that uses a surrogate function for flow distance or for simplified objectives may be entrapped into local optimum; and subsequently lead to a poor layout design due to the multiple-attribute decision making (MADM) nature of a layout design decision. The present study explores the use of MADM approaches in solving a layout design problem. The proposed methodology is illustrated through a practical application from an IC packaging company. Two methods are proposed in solving the case study problem: Technique for order preference by similarity to ideal solution (TOPSIS) and fuzzy TOPSIS. Empirical results showed that the proposed methods are viable approaches in solving a layout design problem. TOPSIS is a viable approach for the case study problem and is suitable for precise value performance ratings. When the performance ratings are vague and imprecise, the fuzzy TOPSIS is a preferred solution method.     

A method for discrete stochastic MADM problems based on the ideal and nadir solutions

Many real life decision making problems can be modeled as discrete stochastic multi-attribute decision making (MADM) problems. A novel method for discrete stochastic MADM problems is developed based on the ideal and nadir solutions as in the classical TOPSIS method. In a stochastic MADM problem, the evaluations of the alternatives with respect to the different attributes are represented by discrete stochastic variables. According to stochastic dominance rules, the probability distributions of the ideal and nadir variates, both are discrete stochastic variables, are defined and determined for a set of discrete stochastic variables. A metric is proposed to measure the distance between two discrete stochastic variables. The ideal solution is a vector of ideal variates and the nadir solution is a vector of nadir variates for the multiple attributes. As in the classical TOPSIS method, the relative closeness of an alternative is determined by its distances from the ideal and nadir solutions. The rankings of the alternatives are determined using the relative closeness. Examples are presented to illustrate the effectiveness of the proposed method. Through the examples, several significant advantages of the proposed method over some existing methods are discussed.     

Possibilistic programming approach for fuzzy multidimensional analysis of preference in group decision making

Multiple attribute decision making (MADM) problems are the most encountered problems in decision making. Fuzziness is inherent in decision making process and linguistic variables are well suited to assessing an alternative on qualitative attributes using fuzzy rating. A few techniques in MADM assess the weights of attributes based on preference information on alternatives. But they are not practical any more when the set of all paired comparison judgments from decision makers (DMs) on attributes are not crisp and also we have to deal with fuzzy decision matrix. This paper investigates the generation of a possibilistic model for multidimensional analysis of preference (LINMAP). The model assesses the fuzzy weights as well as locating the ideal solution with fuzzy decision making preference on attributes and fuzzy decision matrix. All of the information is assumed as triangular fuzzy numbers (TFNs). This method is developed in group decision making environments and formulates the problem as a possibilistic programming with multiple objectives.     

A method for multiple attribute decision-making with the fuzzy preference relation on alternatives

This paper investigates the multiple attribute decision-making (MADM) problem with preference information on alternatives. A new method is proposed to solve the MADM problem, where the decision maker (DM) gives his/her preference on alternatives in a fuzzy relation. To reflect the DM's subjective preference information, a linear goal programming model is constructed to determine the weight vector of attributes and then to rank the alternatives. Finally, a numerical example is used to illustrate the use of the proposed method.     

A multiple attributes decision making method using individual and collaborative attribute data in a fuzzy environment

Although multiple attribute decision making (MADM) problems with both individual attribute data of a single alternative and collaborative attribute data of pairwise alternatives exist in the real world, they have seldom been a focus of research. This paper proposes a MADM method using individual and collaborative attribute data in a fuzzy environment, in which experts use linguistic variables to express their opinions. In the method, first, the evaluation matrix of individual attributes date and the judgment matrix of collaborative attributes data are constructed. Then, the central dominance of one alternative outranking other all alternatives is defined for aggregating the collaborative data. From this, an integrated decision matrix incorporating individual and collaborative attribute data is constructed. Further, based on an extended TOPSIS, the fuzzy positive-ideal solution (FPIS) and the fuzzy negative-ideal solution (FNIS) are determined, and the relative closeness of each alternative to the FPIS and FNIS is calculated to determine the ranking order of all alternatives. Finally, two examples are used to illustrate the applicability of the proposed method.     

An alternative multiple attribute decision making methodology for solving optimal facility layout design selection problems

In the present work, a systematic and an alternative multiple attribute decision making methodology is presented for selection of facility layout design selection problems. The proposed methodology is based on Preference selection index (PSI) method. In the proposed methodology appropriate facility layout design is selected for a given application without considering relative importance between facility layout design selection attributes. Two different types of facility layout design selection problems are examined to demonstrate, validate, and to check the reliability of proposed methodology. In addition, subjective cost benefit analysis is performed to study the benefits to cost to the company. Finally, the study has concluded that the facility layout design selection methodology based on PSI method is simple, logical, and more appropriate for solving the facility layout design selection problems.     

Bidirectional project method for dual hesitant Pythagorean fuzzy multiple attribute decision-making and their application to performance assessment of new rural construction

The dual hesitant Pythagorean fuzzy set (DHPFS) consists of two parts, that is, the membership hesitancy function and the nonmembership hesitancy function, supporting a more exemplary and flexible access to assign values for each element in the domain. It is very suitable to handle the situation that there are various possible values in membership and nonmembership degrees to depict the true circumstance. The bidirectional project method of DHPFS calculates method considered not only the bidirectional projection magnitudes and the distance but also includes angle between objects evaluated. Therefore, this paper proposes a bidirectional project method of DHPFS to handle the multiple attribute decision-making (MADM) problem under the dual hesitant Pythagorean fuzzy environment. Through the measure between each alternative decision matrix and the positive and negative ideal alternative matrix, the ranking order all alternatives can be used to select the best alternative. Furthermore, a model for MADM has been given. Finally, a numerical example for performance assessment of new rural construction has been given to demonstrate the application of bidirectional project method of DHPFS, and we used the dual hesitant Pythagorean weighted Bonferroni mean to compare its reasonable and effectiveness.     

Fuzzy LINMAP method for multiattribute decision making under fuzzy environments

The Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP) developed by Srinivasan and Shocker [V. Srinivasan, A.D. Shocker, Linear programming techniques for multidimensional analysis of preference, Psychometrika 38 (1973) 337–342] is one of the existing well-known methods for multiattribute decision making (MADM) problems. However, the LINMAP only can deal with MADM problems in crisp environments. Fuzziness is inherent in decision data and decision making processes, and linguistic variables are well suited to assessing an alternative on qualitative attributes using fuzzy ratings. The aim of this paper is further extending the LINMAP method to develop a new methodology for solving MADM problems under fuzzy environments. In this methodology, linguistic variables are used to capture fuzziness in decision information and decision making processes by means of a fuzzy decision matrix. A new vertex method is proposed to calculate the distance between trapezium fuzzy number scores. Consistency and inconsistency indices are defined on the basis of preferences between alternatives given by the decision maker. Each alternative is assessed on the basis of its distance to a fuzzy positive ideal solution (FPIS) which is unknown. The FPIS and the weights of attributes are then estimated using a new linear programming model based upon the consistency and inconsistency indices defined. Finally, the distance of each alternative to the FPIS can be calculated to determine the ranking order of all alternatives. A numerical example is examined to demonstrate the implementation process of this methodology. Also it has been proved that the methodology proposed in this paper can deal with MADM problems under not only fuzzy environments but also crisp environments.     

Multiple attribute decision making considering aspiration-levels: A method based on prospect theory

In this paper, a method based on prospect theory is proposed to solve the multiple attribute decision making (MADM) problem considering aspiration-levels of attributes, where attribute values and aspiration-levels are represented in two different formats: crisp numbers and interval numbers. According to the idea of prospect theory, aspiration-levels are firstly regarded as the reference points, and the four possible types for comparing an attribute value with an aspiration-level are described. Then, for all possible cases of the four types, the calculation formulae of gains and losses of alternatives concerning attributes are given. By calculating gain and loss of each alternative, a gain matrix and a loss matrix are constructed, respectively. Further, using the value function proposed in prospect theory and the simple additive weighting method, the overall prospect value of each alternative is calculated. Based on the obtained overall prospect values, a ranking of alternatives can be determined. Finally, a numerical example is used to illustrate the use of the proposed method.     

一种结合主客观信息的多属性决策方法

研究了具有模糊偏好信息的模糊多属性决策问题．提出一种结合主观偏好信息与客观信息的综合特征向量方法．主观偏好信息由决策方案的模糊偏好互补矩阵和属性权重的两两比较互反矩阵组成，客观信息由客观决策矩阵组成．给出了求解模糊多属性决策问题的最小二乘偏差估计方法．通过建立二次规划模型决定属性权重向量，并对方案进行排序．最后，给出了使用该方法的数值例子．     

基于最小二乘支持向量机的多属性决策*

分析多属性决策问题, 提出了基于最小二乘支持向量机的多属性决策方法。该方法从决策问题本身出发,构造学习样本,再通过最小二乘支持向量机拟合出多属性效用函数,从而实现对方案的排序。该模型与支持向量机相比,参数少,算法简单、可靠。最后通过算例表明方法的可行性与有效性。     

Integrating simulation modelling and multi criteria decision making for customer focused scheduling in job shops

Today, customer centricity is an important strategy in business-to-business markets and manufacturing companies need decision support systems that provide adequate information for customer centric applications. This study proposes an integrated decision support system that combines simulation modelling and multi-criteria decision making. More specifically, job shop lot streaming problem is dealt with, and it is aimed to determine the best dispatching rules to schedule batches on machines. To this aim, three renowned performance-oriented criteria; (i) mean flow time, (ii) percentage of tardy orders, (iii) makespan and one customer-oriented criterion; (iv) mean percentage deviation from the customer expectations are considered. Effect of different classical and customer-oriented dispatching rules on these performance criteria are investigated. The performance criteria are weighted using analytical hierarchy process by considering the level of bottleneck resource utilization and customer importance weights. The results reveal that customer-oriented dispatching rules provide better outcomes in case of high level of bottleneck resource utilization and high fluctuation amongst the customer importance weights.     

An approach of TOPSIS technique for developing supplier selection with group decision making under type-2 neutrosophic number

This paper proposes an advanced type of neutrosophic technique, called type 2 neutrosophic numbers, and defines some of its operational rules. The type 2 neutrosophic number weighted averaging operator is determined in order to collective the type 2 neutrosophic number set, inferring some properties of the suggested operator. The operator is employed in a MADM problem to collect the type 2 neutrosophic numbers based classification values of each alternative over the features. The convergent classification values of every alternative are arranged with the assistance of score and accuracy values with the aim to detect the superior alternative. We introduce an illuminating example to confirm the suggested approach for multi attribute decision making issues, ordering the alternatives based on the accuracy function. Selecting an appropriate alternative among the selection options is a difficult activity for decision makers, since it is complicated to express attributes as crisp numbers. To tackle the problem, type 2 neutrosophic numbers can be efficiently used to estimate information in the decision making process. The type 2 neutrosophic numbers can accurately describe real cognitive information. We propose a novel T2NN-TOPSIS strategy combining type 2 neutrosophic numbers and TOPSIS under group decision making as application of T2NN, suggesting a type 2 neutrosophic number expression for linguistic terms. Finally, we provide a real case dealing with a decision making problem based on the proposed T2NN-TOPSIS methodology to prove the efficiency and the applicability of the type 2 neutrosophic number.     

GACRM: A dynamic multi-Attribute decision making approach to large-Scale Web service composition

The problem of QoS-aware Web service composition (QWSC), i.e., how to select from a pool of candidate services to construct a composite service with the best overall QoS performance, is an NP-hard problem. To address a large-scale QWSC problem, a novel method is proposed based on information theory, multi-attribute decision making (MADM) and genetic algorithm. To capture complex judgments, the QWSC problem is formulated into a MADM representation which aims to find acceptable solutions assessed by multiple QoS attributes with varying distributions. To solve the MADM problem for QWSC, each QoS attribute is weighted in both a priori, subjective perspective and a posteriori, information-based perspective based on the discriminative capability of QoS attributes for a dynamic pool of candidate services. Furthermore, to solve the large-scale QWSC problem that conventional MADM methods cannot navigate, we develop a GACRM algorithm by integrating genetic algorithm (GA) with Compromise Ratio Method (CRM). Experiments demonstrate that GACRM obtains nearly the same solution ranking by the CRM but scales much better in terms of computation time for large-scale QWSC problems.     

Simulation-based evaluation of defuzzification-based approaches to fuzzy multiattribute decision making

This paper presents a simulation-based study to evaluate the performance of 12 defuzzification-based approaches for solving the general fuzzy multiattribute decision-making (MADM) problem requiring cardinal ranking of decision alternatives. These approaches are generated based on six defuzzification methods in conjunction with the simple additive weighting (SAW) method and the technique for order preference by similarity to the ideal solution method. The consistency and effectiveness of these approaches are examined in terms of four new objective performance measures, which are based on five evaluation indexes. The simulation result shows that the approaches, which are capable of using all the available information on fuzzy numbers effectively in the defuzzification process, produce more consistent ranking outcomes. In particular, the SAW method with the degree of dominance defuzzification is proved to be the overall best performed approach, which is followed by the SAW method with the area center defuzzification. These findings are of practical significance in real-world settings where the selection of the defuzzification-based approaches is required in solving the general fuzzy MADM problems under specific decision contexts.     

An experimental design approach using TOPSIS method for the selection of computer-integrated manufacturing technologies

The selection of Computer-Integrated Manufacturing (CIM) technologies becomes more complex as the decision makers in the manufacturing organization have to assess a wide range of alternatives based on a set of attributes. Although, a lot of Multi-Attribute Decision-Making (MADM) methods are available to deal with selection applications, this paper aims to explore the applicability of an integrated TOPSIS and DoE method to solve different CIM selection problems in real-time industrial applications. Four CIM selection problems, which include selection of (a) an industrial robot, (b) a rapid prototyping process, (c) a CNC machine tool and (d) plant layout design, are considered in this paper. TOPSIS method and Design of Experiment (DoE) are used together to identify critical selection attributes and their interactions of all these cases by fitting a polynomial to the experimental data in a multiple linear regression analysis. This mathematical model development process involves TOPSIS experiments with the model. The regression meta-model greatly reduced the cost, time and amount of the calculation step in application the TOPSIS model. Application results were validated and shown that they provide good approximations to four decision making problem's results in the literature.     

部分权重信息下对方案有偏好的多属性决策法

研究只有部分权重信息且对方案有偏好的多属性决策问题．首先对方案的偏好信息以互反判断矩阵和互补判断矩阵这两种形式给出的情形，分别建立一个目标规划模型，通过求解这两个模型可确定属性的权重；然后提出一种基于目标规划模型的多属性决策方法；最后通过实例说明了该方法的可行性和有效性。     

Use of multi-attribute decision analysis for designing operations system framework in telecommunications management network

This paper is concerned with the design of an Operations System (OS) framework for implementing a proper telecommunications network in Korea Telecom. Since several types of OS frameworks are under consideration, the telecommunication network manager should now choose the best alternative for use later in establishing the infrastructure of operations systems. The decision making is done in this paper by means of a Multi-Attribute Decision-Making (MADM) analysis. A point to be noted in the present case is that the decision relevant information such as attribute weights and values of alternatives are not known exactly. Rather, these are to be taken in the form of rankings and/or bounds. To deal with such incomplete information, we employ a mathematical programming model-based MADM approach to the choice of an optimal OS framework design. This approach enables us to check dominance of alternatives and to obtain a set of non-dominated alternatives from evaluating five alternatives in reference to Telecommunications Management Network (TMN) standards.

Scope and purpose

Telecommunications Management Network (TMN) can be stated as a globalized and unified skeleton to meet the advanced operational environments of telecommunications networks and to handle heterogeneous and complex network elements. Although there are various subjects that should be addressed to accomplish TMN, we confine our attention to dealing with one important subject, design of an Operations System (OS) framework. This is necessary to manage telecommunications resources including networks in effective manner and for use as guidance in developing individual operations systems and in constructing a network management center.Several types of OS frameworks have been suggested by TMN standards. It is hence needed to evaluate each of these OS frameworks and then to choose an appropriate one in Korean TMN situation. Since this evaluation involves multiple criteria (or attributes), it is realized in this paper by a Multi-Attribute Decision-Making (MADM) analysis approach. The decision relevant information such as trade-off weights and individual criterion values are elicited from expert opinions which, however, is rather incomplete than exact numerical values. The decision making process with the incompletely identified information is presented in this paper.     

Multiple attribute decision-making methods for the dynamic operator allocation problem

This study explores two multiple attribute decision-making (MADM) methods to solve a dynamic operator allocation problem. Both methods use an analytic hierarchy process (AHP) to determine attribute weights a priori. The first method uses a technique for order preference by similarity to ideal solution (TOPSIS). The second method incorporates a fuzzy-based logic that uses linguistic variable representation, fuzzy operation, and fuzzy defuzzification. The TOPSIS uses deterministic performance ratings and attribute weights, whilst the fuzzy-based is a linguistic method. An applied case study drawn from existing literature is used to demonstrate and test findings. The proposed methods systematically evaluate alternative scenarios, with the result indicating promise for solving an operator allocation decision problem.