Neural Computing and Applications

, Volume 31, Issue 10, pp 5901–5916 | Cite as

A modified TOPSIS method based on vague parameterized vague soft sets and its application to supplier selection problems

  • Ganeshsree SelvachandranEmail author
  • Xindong Peng
Original Article


In this paper, we propose an intuitively straightforward extension of the vague soft set model called the vague parameterized vague soft set (vp-VSS). This model generalizes the vague soft set by including the opinions of an expert or a moderator regarding the values of the membership function for the parameters that are considered, in the form of a vague set. The values provided by the experts indicate the threshold values for the membership functions of the elements, i.e., the minimum values that must be ideally satisfied by all the elements for each parameter. This provides a clear indication to the users of these information, and forms a pertinent component of the model, particularly in the decision-making process. Subsequently, we define some operations for this model and examine its properties. Subsequently, we introduce two algorithms based on a modified TOPSIS approach and a weighted aggregation operator approach, both of which are based on our proposed vp-VSS model. These algorithms are then applied in two multi-attribute decision-making problems involving supplier selection and the evaluation of supplier performance. The performance and utility of these algorithms are compared and contrasted in terms of the computational complexity and discriminative power of the algorithms.


Vague soft set TOPSIS Aggregation operator Supplier selection 



The authors would like to express their gratitude to the anonymous reviewers, the editor in charge of this paper, and the Editor-in-Chief for their constructive comments which has helped to improve the quality of this paper. In addition, the first author Ganeshsree Selvachandran would like to gratefully acknowledge the financial assistance received from the Ministry of Education, Malaysia, under Grant No. FRGS/1/2017/STG06/UCSI/03/1 and UCSI University, Kuala Lumpur, Malaysia, under Grant No. Proj-In-FOBIS-014.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.Department of Actuarial Science and Applied Statistics, Faculty of Business and Information ScienceUCSI UniversityCherasMalaysia
  2. 2.School of Information Science and EngineeringShaoguan UniversityShaoguanChina

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