Session Program

Abstract - 2-tuple fuzzy linguistic model can be applied to eliminate vagueness/uncertainty in information. It also helps to deal with non-homogeneous information that occurs during group decision making (GDM) processes. GDM is generally applied to diminish the bias during the decision phase and to reduce the subjectivity of the decision process. Thus, this paper proposes an integrated GDM technique based on 2-tuple linguistic model, quality function deployment (QFD) and the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method. This proposed framework is then applied to a green warehouse selection problem. The originality of this paper comes from its combination of these two methodologies together for the first time in literature in this specific field. The application area indicates its suitability for the proposed methodology.

Abstract - Large-scale group decision making problems exist widely in human being's daily life. In this paper, a new approach to large-scale multi-attribute group decision making with multi-granular unbalanced linguistic information is developed. First, an algorithm is proposed to represent the initial multi-granular unbalanced linguistic information of decision makers with the use of unbalanced linguistic distribution assessments. Based on the gain and loss of an unbalanced linguistic distribution assessment over another, the classical TODIM (an acronym in Portuguese of interactive and multiple attribute decision making) method is then extended to derive a raking of alternatives for large-scale multi-attribute group decision making problems. Finally, an example for talent selection is used to demonstrate the feasibility of the proposed approach.

Abstract - Multi objective linguistic optimization is a useful mathematical technique to solve problems that interdependent criteria. In such problems, values of the objective functions may be unknown at some points, when the link between the variables and the objective functions are defined linguistically through if-then rules. While solving this type of problems, Tsukamoto based reasoning method has proved useful for converting objective function to a crisp form, and then using the resulting objective function to solve by any traditional optimization technique. However, this method suffers from a drawback that the resulting solution is in numeric form whereas it should have been in linguistic form, owing to the linguistic definition of if-then rules. So, here we propose 2-tuple fuzzy linguistic representation model based method for solving the Multi objective linguistic optimization problem. We demonstrate the novelty of our approach through a suitable example. We also prove that the proposed approach generates unique recommendation in linguistic form.

Abstract - Personnel selection is a well-known problem that is made difficult by incomplete and imprecise information about candidate and position compatibility. This paper shows how positions, which satisfy candidate's interests, can be identified with fuzzy linguistic terms and a fuzzy OWA operator. A set of relevant positions aligned with a student's interests is selected using this approach. The implementation of the proposed method is illustrated using a numerical example in a business application.

Abstract - n some real-world multiple attribute decision making (MADM) problems, a decision maker can strategically set attribute weights to obtain her/his desired ranking of alternatives, which is called the strategic weight manipulation of the MADM. Sometimes, the attribute weights are given with imprecise or partial information, which is called incomplete information of attribute weights. In this study, we propose the strategic weight manipulation under incomplete information on attributes weights. Then, a series of mixed 0-1 linear programming models (MLPMs) are proposed to derive a strategic weight vector for a desired ranking of an alternative. Finally, a numerical example is used to demonstrate the validity of our models

Abstract - With the rise of Massive Open Online Courses (MOOCs), online peer marking is an attractive contemporary tool for educational assessment. However its widespread use faces serious challenges, most significantly in the perceived and actual reliability of assessment grades, which can be affected by the ability of peers to mark accurately and the potential for collusion and bias. There exist a number of aggregation approaches for alleviating the impact of biased scores, usually involving either the down-weighting or removal of outliers. Here we investigate the use of the least trimmed squares (LTS) and Huber mean for the aggregation step, comparing their performance to weighting of markers based on divergence from other peers' marks. We design an experimental setup to generate scores and test a number of conditions. Overall we find that for a feasible number of peer markers, when the student pool comprises a significant number of 'biased' markers, outlier removal techniques are likely to result in a number of very unfair assessments, while more standard approaches will have more grades unfairly influenced but to a lesser extent.