International Journal of Industrial Engineering & Production Research

---

  Multiple criteria decision making (MCDM) problem is one of the famous different kinds of decision making problems. In more cases in real situations, determining the exact values for MCDM problems is difficult or impossible. So, the values of alternatives with respect to the criteria or / and the values of criteria weights, are considered as fuzzy values (fuzzy numbers). In such conditions, the conventional crisp approaches for solving MCDM problems tend to be less effective for dealing with the imprecise or vagueness nature of the linguistic assessments. In this situation, the fuzzy MCDM methods are applied for solving MCDM problems. In this paper, we propose a fuzzy TOPSIS (for Order Preference by Similarity to Ideal Solution) method based on left and right scores for fuzzy MCDM problems. To show the applicability of the proposed method, two numerical examples are presented. As a result, our proposed method is precise, easy use and practical for solving MCDM problem with fuzzy data. Moreover, the proposed method considers the decision makers (DMs) preference in the decision making process. It seems that the proposed fuzzy TOPSIS method is flexible and easy to use and has a low computational volume .

---

Implementation of the risk management concepts into construction practice may enhance the performance of project by taking appropriate response actions against identified risks. This research proposes a multi-criteria group decision making approach for the evaluation of different alternative response scenarios. To take into account the uncertainties inherent in evaluation process, fuzzy logic is integrated into the revaluation process. To evaluate alternative response scenarios, first the collective group weight of each criterion is calculated considering opinions of a group consisted of five experts. As each expert has its own ideas, attitudes, knowledge and personalities, different experts will give their preferences in different ways. Fuzzy preference relations are used to unify the opinions of different experts. After computation of collective weights, the best alternative response scenario is selected by the use of proposed fuzzy group decision making methodology which aggregates opinions of different experts. To evaluate the performance of the proposed methodology, it is implemented in a real project and the best alternative responses scenario is selected for one of the identified risks.

---

Today due to the globalization and competitive conditions of the market, decisions are generally made in group and in accordance with different attributes. In addition, all of the information is associated with uncertainty. In such situation, the emergence of inconsistency and facing with the contradictions will be obvious. Having regarded this fact, the development and application of tools that adequately address the uncertainty in decision making process and also be appropriate for group decision making is an important area of multi-criteria decision making (MCDM). Therefore, in this paper, firstly we developed the traditional best-worst method (BWM) and proposed an interval-valued intuitionistic fuzzy best-worst method (IVIFBWM), then introduced a novel approach for fuzzy multi-attribute group decision making based on the proposed method. Finally, in order to demonstrate how the introduced approach can be applied in practice, it is implemented in an Iranian investment company and the experimental results are examined. From the experimental results, we can extract that not only the introduced approach is simple in calculation but also it is convenient in implementation especially in interval-valued intuitionistic fuzzy environments.

---

Preventive maintenance (PM) of machines has the critical role in a factory or enterprise. It decreases number of failures, increases reliability, as well as minimizes costs of production systems.  The managers’ duty of maintenance section is to prioritize machines and then, implement PM programs for them. Since machines have the different measures with respect to the maintenance costs, reliability, mean time between failures (MTBF), availability of spare parts, etc., the machines evaluation problem can be considered as a multiple criteria decision-making (MCDM) problem. Accordingly, the MCDM techniques can be applied to solve them. The aim of this paper is to extend the ELECTRE III (eLimination et choix traduisant la realite´– elimination and choice translation reality) method to interval type-2 fuzzy sets (IT2FSs) using curved (such as Gaussian) membership functions (MFs). The extended ELECTRE III methodology is then utilized to a maintenance group MCDM (GMCDM) matrix including the quantitative and qualitative criteria. In the proposed approach, the criteria weights, the assessment of alternatives with respect to criteria, and the thresholds are stated with Gaussian interval type-2 fuzzy sets (GIT2FSs). In order to show the effectiveness and applicability of the proposed approach, a case study and an illustrative example are exhibited using real decision-making problems. Due to the high correlation coefficients between our method and the others, as well as the results obtained by the proposed method, it can be taken into account as a valid and reliable approach to prioritize machines for PM.