Multicriteria Decision Making (MCDM) is one the most important branches of decision theory. Due to the fact that MCDM methods have the utmost significance in management, scholars try to develop more MCDM methods. Since calculating the weights of criteria is an important step in any MCDM method, increasing the accuracy of weight calculating methods can highly affect these methods. This accuracy can be improved by less pairwise comparison between criteria. To this end, the present study seeks to make a comparison between two new weight calculating techniques, namely BWM and FUCOM in a fuzzy environment using a real-world case study Results of this study shows that FUCOM-F provides more reliable results compared to FBWM since its consistency is less than FBWM by a great amount.

The objective of this paper is to examine the determinants of the supplier selection process with green consideration. Thus, this analysis gathers a collection of factors from established literature of green supplier selection (GSS), including seven categories and 58 attributes. The objective of this research is to classify the key factors which are presented as qualitative information. Fuzzy logic rules are used to transform qualitative expert knowledge into numerical data. Then, we adopt the Delphi method (DM) to filter and rate unneeded factors according to their relevance. The results indicate 24 important factors for the GSS process. Five categories are included: Performance and technology ability, Environmental management, Pollution control, Quality and Service. The most significant factors are recognized as green research and development, eco-design, green image, green packaging and remanufacturing. Finally, the debate is held on the basis of the findings and future research are also recognized and stated.

This research paper is aimed to present a fuzzy Hybrid Multi-criteria decision making (MCDM) methodology for selecting employees. The present study aspires to present the hybrid approach of Fuzzy multiple MCDM techniques with tactical viewpoint to support the recruitment process of wind turbine service technicians. The methodology is based on the application of Fuzzy ARAS (Additive Ratio Assessment) and Fuzzy MOORA (Multi-Objective Optimization on basis of Ratio Analysis) which are integrated through group decision making (GDM) method in the model for selection of wind turbine service technicians’ ranking. Here a group of experts from different fields of expertise are engaged to finalize the decision. Series of tests are conducted regarding physical fitness, technical written test, practical test along with general interview and medical examination to facilitate the final selection using the above techniques. In contrast to single decision making approaches, the proposed group decision making model efficiently supports the wind turbine service technicians ranking process. The effectiveness of the proposed approach manifest from the case study of service technicians required for the maintenance department of wind power plant using Fuzzy ARAS and Fuzzy MOORA. This set of potential technicians is evaluated based on five main criteria.

Wind farms are designed to supply power to the consumers at a minimal price. The cost of wind power production directly or indirectly depends on proper selection of vendors. The present paper highlights a model for selection and ranking of vendors for a wind farm based on fuzzy set theory to determine criteria weights and an additive ratio assessment (ARAS) method to analysis criteria values. The objective of the paper is to establish the ARAS method as an effective method for Vendor selection. A case study is shown to ascertain the proposed method especially when the criteria are interdependent and conflicting in nature. The result is validated with another popular MCDM technique, COPRAS, which shows that the models are effective and applicable, and provide decision makers with better solutions for decision making.

Nowadays, industrial robots are being pervasively used in almost every manufacturing organization for improving operational quality, safety and productivity. Depending on the nature of task to be performed, many varieties of robots are now commercially available from different manufacturers. For efficiently carrying out the designed task, a number of functional attributes of an industrial robot are also simultaneously responsible. Therefore, selection of an appropriate and competitive robot alternative becomes a complicated and equally challenging task for the decision makers. A quite strong model of multi-criteria decision-making is needed to deal with this problem of industrial robot evaluation and selection. In this paper, the applicability of fuzzy axiomatic design (FAD) principles is explored for solving a real time robot selection problem. Seven candidate robots which are commercially available for light assembly operations are evaluated with respect to a mix of nine criteria. All these criteria are either qualitative in nature or expressed as a range of numerical values. Suitability rankings of all the feasible alternatives are derived using FAD methodology, thus establishing it as a systematic and dependable tool for solving industrial robot selection problems in fuzzy environment.

The dynamics of human resource recruitment and training in an uncertain environment creates a challenge for many policy makers in various organisations. In the presence of fuzzy manpower demand and training capacity, many companies fear losing critical human resources when their employees leave. As such, the development of effective dynamic policies for recruitment and training in a fuzzy dynamic environment is imperative. In this frame of mind, a fuzzy systems dynamics modelling approach is proposed to enable the policy maker to develop reliable dynamic policies relating recruitment, training, and available skills, from a systems perspective. It is anticipated in this study that fuzzy system dynamics and optimization approach would help organizations to design effective manpower policies and strategies.

The way orders are accepted or rejected is the most important factor in customer satisfaction and success of make-to-order systems. The incoming orders to such organizations have certain delivery date in which the customer expects the order to be fulfilled and delivered. In some cases, unexpectedly increased orders exceed the existing capacity for on time fulfillment. In addition to rejection of order, as a typical choice, other options like outsourcing and capacity expansion are available to compensate for capacity shortage and deliver incoming orders according to schedule. However, each of the proposed options is superior in one or more criteria and so selecting the best one is not simply possible. The main goal of this study is to provide managers with a comprehensive, systematic, and applicable approach to evaluate and select the best of the existing options. For this purpose, a model comprised of some multi-criteria techniques is delivered. Our proposed model is a blend of FAHP and FTOPSIS methods. In this model, FAHP is first used to determine the weight of criteria and then Fuzzy-TOPSIS (FTOPSIS) is employed to rank the options. Finally, the proposed model is applied on an actual case to assess and examine its efficiency.

Now-a-days, the offer of credit period to the retailer for settling the account for the units purchased by the supplier is considered to be the most beneficial policy. In this article, an attempt is made to formulate an economic order quantity model under fuzzy environment where delay in payment for the retailer is permissible. The demand rate, ordering cost and selling price per item are taken as triangular fuzzy numbers. The ?-cut representation method is used to calculate the optimum cycle time and total optimum cost. The optimum cycle time and total optimum cost in fuzzy sense is de-fuzzified using the centre of gravity method.

Decisions related to acceptance or rejection of orders play an important role in companies engaged in make-to-order production. The incoming orders have a specific delivery date by which the customer expects the due date to be met and the order delivered. In some cases the level of input orders exceeds beyond the existing capacity. In such situations the main concern is to decide which orders must be accepted and which ones rejected taking into account the available production capacity. This paper prioritises the input orders according to a comprehensive and systematic multi criteria decision making (MCDM) model. It then proceeds with making decisions to either accept or reject orders according to the calculated prioritises and production constraints. Ultimately the optimum list of orders for acceptance is determined. The proposed model is a combination of two techniques of Fuzzy Analytical Hierarchy Process (FAHP) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). In this model FAHP is used to determine the weights of criteria and TOPSIS is used for prioritizing the orders. Finally the proposed model is tested for its efficiency by application to a real case.