Hierarchical Group Compromise Ranking Methodology Based on Euclidean–Hausdorff Distance Measure Under Uncertainty: An Application to Facility Location Selection Problem

Document Type : Original Manuscript


1 Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran

2 Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran

3 Department of Industrial Engineering, Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran



Proposing a hierarchical group compromise method can be regarded as a one of major multi-attributes decision-making tool that can be introduced to rank the possible alternatives among conflict criteria. Decision makers’ (DMs’) judgments are considered as imprecise or fuzzy in complex and hesitant situations. In the group decision making, an aggregation of DMs’ judgments and fuzzy group compromise ranking is more capable and powerful than the classical compromise ranking. This research extends a new hierarchical group compromise ranking methodology under a hesitant fuzzy (HF)environment to handle uncertainty, in which for the margin of error, the DMs could assign the opinions in several membership degrees for an element. The hesitant fuzzy set (HFS)is taken into account for the process of the proposed hierarchical group compromise ranking methodology, namely HFHG-CR, and for avoiding the data loss, the DMs’ opinions with risk preferences are considered for each step separately. Also, the Euclidean–Hausdorff distance measure is utilized in a new proposed index for calculating the average group score, worst group score and compromise measure regarding each DM. A new ranking index is presented for final compromise solution for the evaluation. Proposed HFHG-CR methodology is applied to a practical example for a facility location selection problem, i.e. cross-dock location problem, to show the validation and application.

Graphical Abstract

Hierarchical Group Compromise Ranking Methodology Based on Euclidean–Hausdorff Distance Measure Under Uncertainty: An Application to Facility Location Selection Problem


  • Considering experts’ opinions with risk preferences via HFSs in two ranking processes
  • Proposing a new index for computing average and worst group scores by HF-Euclidean–Hausdorff distance measure
  • Proposing a new ranking index for calculating compromise measure by experts’ judgments
  • Aggregating experts’ opinions for prevention of data loss at the end of proposed methodology


Main Subjects

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