Multiple Attribute Decision Making

In response to increasing inflexible customer demands and to improve the competitive advantage, industrial organizations have to adopt strategies to achieve cost reduction, continual quality improvement, increased customer service and on-time delivery performance. Selection of the most suitable plant or facility layout design for an organization is one among the most important strategic issues to fulfill all these above-mentioned objectives. Nowadays, many industrial organizations have come to realize the importance of proper selection of the plant or facility layout design to survive in the global competitive market. Selecting the proper layout design from a given set of candidate alternatives is a difficult task, as many potential qualitative and quantitative criteria need to be considered. This paper proposes a Euclidean distance based approach (WEDBA) as a multiple attribute decision making method to deal with the complex plant or facility layout design problems of the industrial environment. Three examples are included to illustrate the approach.

In multi-attribute decision making, the object things are complex, uncertain and Human thinking is ambiguous[1- 17]. Therefore, interval numbers are usually more adequate or sufficient to model real-life decision problems than real numbers. Many methods have been proposed for dealing with the problem of multiple attribute decision making which the attribute values are given in terms of interval numbers[1-4], a majority of which are usually treated the attribute values as random variables or subjected to the random distribution. But, in other papers, they investigated the attribute values are subjected to the normal distribution, and they also had got some achievements [5-8]. However, the assumption is too strong to match decision behaviors in the real world. Under the condition that the overall values of alternatives in interval number evaluated are subjected to the normal distribution, Zhang and Fang [5] proposed the concept on the possibility for comparing two interval numbers, and then set up the according pair wise comparison matrix on alternatives. Liu, Chen and Ge [6] introduced that the interval number which obeys normal distribution is used in multi-attribute decision making. Wang and Xiao [7] defined some new aggregation operators, such as the NDINWAA operator, the NDINOWA operator and the NDINHA operator, and then developed an approach for solving multiple attribute group decision making based on normal distribution interval number with incomplete information. Wang and Yang [8] proposed the NDNWAA operator and the DNDNWAA operator and suggested a method for solving dynamic stochastic multiple attribute decision making with incomplete certain information.

Abstract: This article addresses reinsurance decision making process, which involves the insurance company and the reinsurance company, and is negotiated through reinsurance intermediaries. The article proposes a decision flow to model the reinsurance design and selection process. In contrast to existing literature on pure proportional reinsurance or stop-loss reinsurance, this article focuses on the combination into Proportional-Stop-loss reinsurance design which better addresses interest of both parties. In terms of methodology, the significant contribution of the study is to incorporate Multiple Attribute Decision Making (MADM) into modelling the reinsurance selection. The Multi- Objective Decision Making (MODM) model is applied in designing reinsurance alternatives. Then MADM is applied to aid insurance companies in choosing the most appropriate reinsurance contract. To illustrate the feasibility of incorporating intelligent decision supporting system in reinsurance market, the study includes a numerical case study using simulation software @Risk in modeling insurance claims, and programming in MATLAB to realize MADM. Managerial implications could be drawn from the case study results. More specifically, when choosing the most appropriate reinsurance, insurance companies should base their decision on multiple measurements instead of single-criteria decision making models for their decisions to be more robust.

Vagueness in the scientific studies presents a challenging dimension. Intuitionistic fuzzy set theory has emerged as a tool for its characterization. There is need to associate measures which can measure vagueness and differences in the underlying characterizing IFSs. In the present paper we introduce an information theoretic divergence measure, called intuitionistic fuzzy Jensen-Rényi divergence. It is a difference measure in the setting of intuitionistic fuzzy set theory, involving parameters that provide flexibility and choice. The strength of the new measure lies in its properties and applications. An approach to multiple-attribute decision making based on intuitionistic fuzzy Jensen-Rényi divergence is proposed. A numerical example illustrates the application of the new measure and the role of various parameters therein to multipleattribute decision making problem formulated in terms of intuitionistic fuzzy sets.

Recently, most businesses have introduced a system for improving their responsibility to the customers in terms of job improvement. For example, small-quantity batch production increases cost but improve efficiency of management. Companies have been introduced the balanced scorecard to evaluate their management as part of improvement, while they suffer from many trials and errors. Many businesses still have difficulty in introducing balance scorecard concept in their process, but we suggest a method to successfully introduce the balance scorecard. This study aims to suggest a new performance measurement model reflecting relative importance of the key performance indicators for each factor. Our model is applied to several companies in real-world to validate the new model. Also, our study proposes a methodology for an adequate performance measurement using multiple attribute decision making.

Abstract The purpose of this paper is to present the possibility of replacing physical unit cost in transportation or distribution problems by an aggregate coefficient, getting qualitative and subjective considerations involved. The model for constructing aggregate cost is a two stage multiple attribute decision-making problems. In the first stage supply points, demand points and routes of transportation are alternatives and have to be weighted against their own attributes. In the second stage, the alternatives are placed as attributes in a new matrix and the unit aggregate costs will be the new alternatives. Some heuristic techniques are developed for tradeoffs between attributes. Experts and decision-makers do tradeoff. The results are compared with the simple physical costs.

In the process of the multiple attribute decision making (MADM), the decision making information, given by the decision makers, often takes the form of the linguistic variables, because of the complexity and uncertainty of the objective things, and the ambiguity of human thinking. Therefore, the MADM under the linguistic context is an interesting research topic which has been receiving more and more attention in recent years [1-4]. Some operators were widely used to aggregate the decision making information in the process of the MADM. Bordogna et al. [5] developed a model within fuzzy set theory by the linguistic ordered weighted average ( LOWA ) operators for the group decision making in the linguistic context. Xu [6] proposed an approach to solve the multiple attribute group decision making problems with the uncertain linguistic information, based on the uncertain linguistic ordered weighted averaging ( ULOWA ) operator and the uncertain linguistic hybrid aggregation ( ULHA ) operator. Wu and Chen [7] introduced the linguistic weighted arithmetic averaging ( LWAA ) operator to aggregate the decision making information which took the form of the linguistic variables. Xu [8] developed some operators for aggregating the triangular fuzzy linguistic variables, such as the fuzzy linguistic averaging ( FLA ) operator, the fuzzy linguistic weighted

Thus we propose a new multiple attribute decision making method under intuitionistic fuzzy environment with the help of knowledge measure. This method overcomes all the drawbacks in the existing methods. Further this can be extended to type 2 interval fuzzy sets also.

In this paper we have proposed a method for similarity measure of interval valued vague sets and extended the work of Szmidt, Kacprzyk and Zeshui Xu etc. We applied these similarity measure results to multiple attribute decision making. A numerical example of an engineer, hire by a high technology company has been taken to illustrate the application of these developments. We have considered the eight candidates A A A 1 , 2 ,........, A 8 and six benefit criteria for the illustration of this technique. Finally we knew that, candidate A 5 was the best one who had obtained by all the similarity measures. It is not possible that the belongingness of an element in a set is a single value, but it is an interval and same for not belongingness of element in the set. In this paper we have proposed a method for the development of some similarity measure for interval valued vague sets and define the positive and negative ideal of interval valued vague sets, and applied the similarity measures to multiple attribute decision making based on vague information.

The focus of this paper is to investigate the multiple attribute decision making problems using rough intuitionistic fuzzy information. Based on evidence theory and grey relational analysis (GRA) technique, we design a new method for decision making problems. We verify the developed approach and to demonstrate its practicality and its effectiveness by an example.

The MADM methods proposed in this paper are validated using two case studies of the selection problems of the manufacturing environment. The uniqueness of the proposed decision making methods presented in this paper is that they offer general procedures those are applicable to diverse selection problems encountered in the manufacturing environment that incorporate vagueness and a number of selection attributes. The methods are capable of handling the subjective as well as objective type attribute data. The decision making methods reported here can simultaneously consider any number of quantitative and qualitative selection attributes and helps to obtain the preference index which evaluates and ranks alternatives for a given selection problem. The methods are logical and convenient to implement. These methods can be extended to any other decision making problems of the industrial environment.

In recent years, multi-criteria evaluation methods have been widely used in solving both theoretical and practical problems (Zavadskas, Turskis, 2008). Actually, these methods are universal. They allow us to quantitatively evaluate any complicated object described by a set of criteria. Another advantage of these methods is their ability to combine both maximizing and minimizing attributes expressed in various dimensions into one integrated criterion. The maximizing attributes imply that, if their values are growing, the situation is getting better, while for minimizing attributes this means a worsening situation. The integration is achieved by normalization. Normalization helps to convert all the attribute values into no dimensional, i.e. comparable quantities (Ginevicius, 2008; Turskis et al, 2009).

An approach to multiple attribute decision making (MADM) with an interval-valued Intuitionistic fuzzy set is developed based on the combined concept of GRA and minimum of regret methods. Grey relational analysis (GRA) was originally developed by Deng in 1989. GRA method has been successfully applied and it is the best method to make decisions in a business environment. The major advantages of the GRA method are, the results depend on the original data and the calculations are simple and straightforward [Wei, G. W. (2010)]. In order to derive the attribute weights in GRA method, an optimization model is made based on the concept of the score function and the principle of minimization of regret. First, obtain the weights using minimization of regret method and then rank and select the alternatives using Grey Relational Analysis [Ozturkoglu, Y., & Esendemir, E. (2014)].

Abstract — India is the second largest bicycle manufacturer in the world, next only to China. The Chinese bicycle industry has already been successful in cornering almost 25 per cent share in fancy bicycle market in India which is a major cause of worry for the small-scale bicycle parts manufacturers in India. An attempt has been made to provide a comprehensive framework for the selection process of bicycle chain material in Indian manufacturing scenario for strategic success. Multiple attribute decision-making (MADM) methods have been applied to rank out the alternatives. Material AISI 1038 has been ranked first by using both the techniques.

This is felt to be less effective because of the inaccuracy of the criteria used in decision making for scholarship recipients due to the lack of testing of these criteria. To facilitate the decision making in determining the students who deserve to receive the scholarship required a decision support system that examines the criteria as one of the conditions in the acceptance of the scholarship. This criterion is tested using Fuzzy MADM (Multiple Attribute Decision Making) method. Fuzzy MADM is used to find alternatives from a number of alternatives with certain criteria. Research is done by finding the weight value for each attribute, then done the ranking process to determine the given alternatives. The process of determining scholarships with Fuzzy MADM can accelerate the ranking process, reduce errors in determining scholarship recipients, and assist the selection team in determining scholarship recipients [3].

In the extent of natural science, operations research, economics, management science, military affairs, and urban planning, NSs have a broad application. They also can be applied todecision making problems when the ambiguity and complexity of the attributes make the problems impossible to be expressed or valued with real numbers. There were some studies of multi-criteria decision-making methods based on SVNS [16–27], INs [28–35],BNs [36–38], generalized neutrosophic soft set [39,40], neutrosophic refined set [41–44], and triangular fuzzy neutrosophic number set (TFNNs) [45–49]. This paper presents an overview of NSs and some of the most significant instances and extensions of NS, as well as the application of these models in multiple attribute decision-making (MADM) problems. The neutrosophic models that will be reviewed in this paper include theSVNS [14], INS [15], BNS [12], ReNS [31], and the aggregation of TFNS [47].The neutrosophic set has been also applied to various applications [50] such as e-learning [51], medical image denoising [52], Strogatz’s spirit [53].

In multiple attribute decision making (MADM) problem, a decision maker (DM) has to choose the best alternative that satisfies the evaluation criteria among a set of candidate solutions. It is generally hard to find an alternative that meets all the criteria simultaneously, so a better solution is preferred. The SAW method was developed for multi-criteria optimization of complex systems [6,13,18]. This method focuses on ranking and selecting from a set of alternatives in the presence of conflicting criteria. Multi-criteria optimization is the process of determining the best feasible solution according to the established criteria (representing different effects). Practical problems are often characterized by several non-commensurable and conflicting criteria and there may be no solution satisfying all criteria simultaneously. Thus, the solution is a set of non-inferior solutions, or a compromise solution according to the decision maker’s preferences. The compromise solution was established by Zeleny [19] for a problem with conflicting criteria and it can help the decision makers to reach a final solution. In classical MADM methods, the ratings and the weights of the

The above evaluation system based on quantitative indexes makes a quantitative assessment on “important” ser- vice agents. Revaluation which combines some key qualitative indexes should be made on condition that quan- titative evaluation meets the requirement. The key qualitative indexes should include the following five aspects. They are quality of service, level of technology, satisfaction of users, relationship of cooperation and capacity to coordinate. The fuzzy multiple attribute decision making method is used to qualitatively evaluate “important” service agents in the following part.

In the light of universality of uncertainty, we propose a decision making model in completed information system. Con- sidering the attribute reduction, attribute importance and mismatched information, a multiple attribute decision making model based on importance of attribute is constructed. First of all, decision table is obtained by the knowledge known and deleting reduced attributes. Also, attributes value reduction obtained to simplify the decision table and rules is ex- tracted. Then, rules are utilized to make decision for a new problem. Finally, an example is advanced to illustrate our model.

In the present paper, a new entropy of order α and type β on Interval-Valued Intutionistic Fuzzy Sets (IVIFSs) along with their proofs of validity is proposed. It has been proved that the proposed entropy has monotonic decreasing behavior with respect to α and β . Further, a new algorithm for multiple attribute decision making method (MADM) has been provided using the benefit attributes and cost attribute weights on the proposed entropy, where the al- ternatives on attributes are expressed by interval-valued intuition- istic fuzzy sets (IVIFS). The information about attribute weight is unknown. Finally, numerical example for illustrating the proposed methodology has also been provided to illustrate the applicability and validity of the newly proposed method.