An attitudinal consensus method under uncertainty in 3PRLP selection

To underpin a reverse logistics (RL) outsourcing decision, this study aims to investigate a third-party reverse logistics provider (3PRLP) selection problem under the framework of group decision making (GDM). Group attitude towards distinctive opinions may affect the time and cost on consensus reaching process (CRP). Therefore, an attitudinal consensus method under uncertainty is established and composed of three parts: initialization process, consensus process and selection process. In initialization process, considering each expert's risk preference, a transformation rule is deployed to compute semantics possibility distribution of general comparative linguistic expressions (GCLEs) based on the numerical counterpart of linguistic assessment. In consensus process, three group attitudes-oriented consensus policies are qualitatively presented, including maximum, indifference and minimum disagreement consensus policies. Considering the feasible interval of attitudinal parameter, a new consensus measure under uncertainty, i.e., attitudinal consensus acceptability index (ACAI), is quantitively defined by stochastic acceptability analysis. To resolve the issue that the consensus degree fails to reach a predefined level, an automatic feedback mechanism is designed, and is supported by a consensus improvement algorithm evolving GCLEs’ possibility distribution. In selection process, to identify the criteria weight, incomplete information such as pairwise comparisons of criteria are characterized by ordinal regression-like linear programming. TODIM (an acronym in Portuguese of interactive and multiple attribute decision making) is extended on fuzzy measure and then acted as rank function in stochastic acceptability analysis to explore the uncertainty of criteria preference. Lastly, 3PRLP selection for an automobile manufacturing firm is conducted to demonstrate the flexibility and practicability of our method along with sensitivity analysis and comparative analysis.