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

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Abstract

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

VIKOR:

VIse kriterijumska optimizacija kompromisno resenje

PL-VIKOR:

Probabilistic linguistic-vise kriterijumska optimizacija kompromisno resenje

PLTSs:

Probabilistic linguistic term sets

MCGDM:

Multiple criteria group decision-making

LTS:

Linguistic term set

FTA:

Fault-tree analysis

AHP:

Analytic hierarchy process

TBM:

Tunnel boring machine

FMEA:

Failure modes and effects analysis

DEMATEL:

Decision-Making Trial and Evaluation Laboratory

PL-TOPSIS:

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

PL-MULTIMOORA:

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

PL-LINMAP:

Probabilistic linguistic-linear programming techniques for multidimensional analysis of preference

PL-GLDS:

Probabilistic linguistic-gained and lost dominance score

PL-ORESTE:

Probabilistic linguistic-organisation rangement etAynhése de données relationnelles

PL-ELECTRE:

Probabilistic linguistic-elimination et choix traduisant la realite

PL-PROMETHEE:

Probabilistic linguistic-preference ranking organization method for enrichment evaluations

PL-QUALIFLEX:

Probabilistic linguistic-qualitative flexible

PL-GRA:

Probabilistic linguistic-grey relational analysis

PL-DNMA:

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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Q.C. proposed the original idea and conceived the study. X.G. was responsible for developing the method. L.L. and X.G. were responsible for collecting and analyzing the data. X.G. and L.L. wrote the first draft of the article. X.L. revised the paper.

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Correspondence to Xunjie Gou.

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Li, L., Chen, Q., Li, X. et al. An Improved PL-VIKOR Model for Risk Evaluation of Technological Innovation Projects with Probabilistic Linguistic Term Sets. Int. J. Fuzzy Syst. 23, 419–433 (2021). https://doi.org/10.1007/s40815-020-00971-1

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