# Multiple-Attribute Decision Making ELECTRE II Method under Bipolar Fuzzy Model
## 1 Introduction  
Multiple-criteria decision making (MCDM) techniques are concerned with designing and evaluating the structure of decision and planning problems involving multiple criteria. These models accommodate the procedure of determining the most convenient option among the reasonable alternatives and have a great role in cost minimization, time saving and accumulating accurate decisions. MCDM models are established on the basis of variations of beliefs regarding to decision maker's choice and utilize a particular type of preference intelligence system. The decision making problems in different fields including economics, business management, information technology, social sciences and medical sciences are structured and solved using different MCDM models. MCDM based systematic approach has seen an excessive amount of use over the last several decades and a variety of MCDM models containing Analytic Hierarchy Process (AHP) [1], Technique for the Order of Preference by Similarity to an Ideal Solution(TOPSIS) [2], Vlekriterijumsko Kompromisno Rangiranje (VIKOR) meaning multi-criteria optimization and compromise solution [3], ELimination and Choice Translating REality (ELECTRE) [4] and Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE) [5] have been presented to rank alternatives or to find the kernel solution. In classical MCDM methods, the preference ratings of alternatives with respect to criteriaand the attribute weights are given in the form of precise information or data. To cope with such type of problems having ambiguous information and imprecise in knowledge, [PERSON] and [PERSON] (2016) in 1970 introduced the decision making model for an uncertain system using the concept of fuzzy sets. Since then, the decision making using fuzzy sets has become an interesting field of research for practitioners. A series of outranking methods is used to rank a set of alternatives. The ELECTRE method as well as its derivatives has a significant role and importance in this class of MCDM methods. Firstly, ELECTRE was presented by [PERSON] et al. (2016) in 1966 to show the preference of an alternative over the other alternatives by pairwise comparison of alternatives through outranking relations. These relations are used to produce the most possibly accurate and desirable set of alternatives by the elimination of actions or alternatives which are ranked by other alternatives under the influence of conflicting criteria. Later on, this method was explained in detail by [PERSON] (2016) in 1968 and renamed as ELECTRE I. The basic concept of ELECTRE method is to determine the concordance and discordance sets that represent the relative advantages and disadvantages of alternatives, respectively. After the introduction of ELECTRE I method, many other variants or types of the classical ELECTRE method have been obtained, namely ELECTRE II, ELECTRE III, ELECTRE IV, ELECTRE IS and ELECTRE TRI. [PERSON] and [PERSON] (2016) developed a new fuzzy outranking approach by developing the ELECTER I method using fuzzy information to deal with ambiguous, indefinite and linguistic information given by a group of decision makers. Bipolar neutrosophic ELECTRE I and Pythagorean fuzzy ELECTRE I models were introduced by [PERSON] et al. (2016); [PERSON] et al. (2016) to solve the MCDM problems having bipolar neutrosophic and Pythagorean fuzzy environment, respectively. There exist many ELECTRE methods in the literature, which are used in various decision-making dimensions including business management ([PERSON] [30] presented a novel trapezoidal bipolar fuzzy TOPSIS method for group decision-making. [PERSON] et al. [31] used bipolar fuzzy TOPSIS and bipolar fuzzy ELECTRE I methods in medical diagnosis. Recently, [PERSON] et al. [32] presented a group decision making method based on VIKOR technique using trapezoidal bipolar fuzzy information. [PERSON] et al. [33] proposed the TOPSIS method under pythagorean fuzzy environment for group decision making.