Multicriteria decision‐making approaches are receiving more and more attention with the increase of expectations from decision makers in variety of fields. The growth in applying such approaches has led to identifying their strengths as well as their shortcomings. Passive and active compensability multicriteria analysis (PACMAN) is one of the frequently used approaches which has the capability to consider compensation in describing intercriteria relations in multicriteria decision‐making problems. This methodology is well formed and rationally structured in the first two phases, in which the problem is formulated and the decisive indices are obtained. However, it has some shortcomings in the idea of concluding the process of solution in its last phase. In the current study, we review the methodology, discuss its possible shortcomings, and propose an approach based on the combination of PACMAN and linear programming technique for multidimensional analysis of preference (LINMAP). The proposed approach which is taken to evaluate the PACMAN and LINMAP methodologies can help researchers and decision makers who seek an accurate perspective to evaluate a multicriteria decision‐making methodology.

Purpose: The aim of this study is applying a new method for Industrial robotic system selection. Design/methodology/approach: In this paper, the weights of each criterion are calculated using fuzzy Shannon's Entropy. After that, fuzzy TOPSIS is utilized to rank the alternatives. After that we compare the result of Fuzzy TOPSIS with Fuzzy VIKOR method. Then we select the best Industrial Robotic System based on these results. Findings: The outcome of this research is ranking and selecting industrial robotic systems with the help of Fuzzy Shannon's Entropy and Fuzzy TOPSIS techniques. Originality/value: This paper offers a new integrated method for industrial robotic system selection.