An Improved PL-VIKOR Model for Risk Evaluation of Technological Innovation Projects with Probabilistic Linguistic Term Sets

Risk evaluation is a primary but important task for technological innovation projects and this task is a multiple criteria group decision-making (MCGDM) process with probabilistic uncertainty and fuzzy uncertainty. Compromise programming decision-making methods with probabilistic linguistic term sets (PLTSs) are more appropriate for risk evaluation of technological innovation projects. This paper proposes a new approach named improved probabilistic linguistic-vise kriterijumska optimizacija kompromisno resenje (PL-VIKOR) method with probabilistic linguistic term sets for risk evaluation of technological innovation projects. Firstly, by fully considering both the relationship between each alternative and the positive ideal solution and the relationship between each alternative and negative ideal solution, the improved PL-VIKOR method for dealing with MCGDM problems is developed to make up the deficiency of the traditional PL-VIKOR method. Then, the improved PL-VIKOR method is applied to solve a practical MCGDM problem with probabilistic linguistic term sets involving the risk evaluation of technologically innovative projects for venture capital. Finally, we make some comparative analyses between the improved PL-VIKOR method and some existing methods to analyze the advantages and disadvantages of the proposed method. The results reflect that the improved PL-VIKOR method is more reasonable when calculating the distance measure between two PLTSs, and it can make the risk evaluation of technological innovation project MCGDM with PLTSs more objective.

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Abbreviations

VIse kriterijumska optimizacija kompromisno resenje

Probabilistic linguistic-vise kriterijumska optimizacija kompromisno resenje

Probabilistic linguistic term sets

Multiple criteria group decision-making

Linguistic term set

Analytic hierarchy process

Tunnel boring machine

Failure modes and effects analysis

Decision-Making Trial and Evaluation Laboratory

Probabilistic linguistic-technique for order performance by similarity to ideal solution

Probabilistic linguistic-multi-objective analysis by ratio analysis plus the full multiplicative form

Probabilistic linguistic-linear programming techniques for multidimensional analysis of preference

Probabilistic linguistic-gained and lost dominance score

Probabilistic linguistic-organisation rangement etAynhése de données relationnelles

Probabilistic linguistic-elimination et choix traduisant la realite

Probabilistic linguistic-preference ranking organization method for enrichment evaluations

Probabilistic linguistic-qualitative flexible

Probabilistic linguistic-grey relational analysis

Probabilistic linguistic-double normalization-based multiple aggregation

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Funding

The work was supported by the National Social Science Foundation of China (Grant No. 16BGL024), Sichuan Province System Science and Enterprise Development Research Center (Grant Nos. Xq20B03 and Xq16C13), and the Fundamental Research Funds for the Central Universities (Grant Nos. YJ202015 and 2020ZY-SX-C01).

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Authors and Affiliations

  1. Business School, Sichuan University, Chengdu, 610064, Sichuan, China Liping Li, Qisheng Chen, Xiaofeng Li & Xunjie Gou