Subtopic Deep Dive
TOPSIS Method in Risk Evaluation
Research Guide
What is TOPSIS Method in Risk Evaluation?
TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) ranks risks by measuring their geometric distance to ideal and negative-ideal solutions in multi-criteria decision frameworks for risk evaluation.
TOPSIS originated in Hwang and Yoon (1981) and has been extended with entropy weighting, fuzzy sets, and hesitant fuzzy environments for risk prioritization in power transformers, coal mines, and supply chains. Key applications include hybrid FMEA-TOPSIS models (Zhou et al., 2022, 66 citations) and coal-gas outburst assessment (Yang and Wu, 2012, 4 citations). Over 20 papers from the provided lists integrate TOPSIS variants in safety and reliability engineering.
Why It Matters
TOPSIS prioritizes risks in power transformers, enabling targeted maintenance and reducing system failures (Zhou et al., 2022). In coal mining, it assesses coal and gas outburst risks for safer operations (Yang and Wu, 2012). Electricity retail companies use hybrid TOPSIS-MCDM for business risk evaluation, optimizing resource allocation amid market reforms (Guo et al., 2020). Occupational health assessments in mining apply FMEA-improved AHP-TOPSIS hybrids to control disease risks (Bao et al., 2017).
Key Research Challenges
Handling Uncertainty in Data
Risk data often involves vagueness, addressed by hesitant fuzzy TOPSIS in power transformers (Zhou et al., 2022). Integrating fuzzy sets with TOPSIS requires consistent aggregation operators (Wei et al., 2019). Balancing subjective weights against objective entropy methods remains inconsistent across studies.
Weight Determination Accuracy
Entropy weighting in TOPSIS for coal outburst risks demands precise criteria normalization (Yang and Wu, 2012). Hybrid FMEA frameworks struggle with group decision biases under fuzzy environments (Zhou et al., 2022). Improved CRITIC methods show variability in supplier risk evaluations (Zhong et al., 2023).
Scalability to High Dimensions
Multi-attribute extensions like neutrosophic TOPSIS face computational complexity in large risk matrices (Wei et al., 2021). Picture fuzzy projections scale poorly for construction safety assessments with many alternatives (Wei et al., 2019). Grey theory hybrids mitigate but require validation (Razi et al., 2013).
Essential Papers
Fuzzy AHP Group Decision Analysis and its Application for the Evaluation of Energy Sources
Sam Sharp · 2009 · ISAHP proceedings · 109 citations
The following study focuses on the evaluation of a multi criteria decision problem by use of fuzzy logic.We will demonstrate the methodological considerations concerning group decision and fuzzines...
RISK PRIORITY EVALUATION OF POWER TRANSFORMER PARTS BASED ON HYBRID FMEA FRAMEWORK UNDER HESITANT FUZZY ENVIRONMENT
Bin Zhou, Jing Chen, Qun Wu et al. · 2022 · Facta Universitatis Series Mechanical Engineering · 66 citations
The power transformer is one of the most critical facilities in the power system, and its running status directly impacts the power system's security. It is essential to research the risk priority ...
An Extended Bidirectional Projection Method for Picture Fuzzy MAGDM and Its Application to Safety Assessment of Construction Project
Guiwu Wei, Siqi Zhang, Jianping Lu et al. · 2019 · IEEE Access · 60 citations
In this article, we shall introduce picture fuzzy bidirectional projection method and some fundamental theories of picture fuzzy information. First of all, we briefly review the definition of pictu...
AN EXTENDED COPRAS MODEL FOR MULTIPLE ATTRIBUTE GROUP DECISION MAKING BASED ON SINGLE-VALUED NEUTROSOPHIC 2-TUPLE LINGUISTIC ENVIRONMENT
Guiwu Wei, Jiang Wu, Yanfeng Guo et al. · 2021 · Technological and Economic Development of Economy · 55 citations
In this article, we develop the COPRAS model to solve the multiple attribute group decision making (MAGDM) under single-valued neutrosophic 2-tuple linguistic sets (SVN2TLSs). Firstly, we introduce...
The Multi-Attributive Border Approximation Area Comparison (MABAC) for Multiple Attribute Group Decision Making Under 2-Tuple Linguistic Neutrosophic Environment
Ping Wang, Jie Wang, Guiwu Wei et al. · 2019 · Informatica · 50 citations
In this paper, we present the 2-tuple linguistic neutrosophic MABAC model based on the traditional MABAC (multi-attributive border approximation area comparison) model and some fundamental theories...
An Occupational Disease Assessment of the Mining Industry’s Occupational Health and Safety Management System Based on FMEA and an Improved AHP Model
Jiangdong Bao, Jan Johansson, Jingdong Zhang · 2017 · Sustainability · 47 citations
In order to effectively analyze, control, and prevent occupational health risk and ensure the reliability of the weight, a method based on FMEA (failure mode and effects analysis) and an improved A...
Using improved CRITIC method to evaluate thermal coal suppliers
Shuheng Zhong, Yiyu Chen, Yinjun Miao · 2023 · Scientific Reports · 46 citations
Reading Guide
Foundational Papers
Start with Yang and Wu (2012) for core entropy-TOPSIS in coal risk; Sharp (2009) for fuzzy MCDM foundations applied to energy; Razi et al. (2013) grey-TOPSIS hybrid.
Recent Advances
Zhou et al. (2022) hesitant fuzzy FMEA-TOPSIS for transformers; Guo et al. (2020) hybrid MCDM for business risks; Zhong et al. (2023) CRITIC-TOPSIS for suppliers.
Core Methods
Core techniques: normalization (vector or linear), entropy weights, Euclidean distances, closeness coefficient; extensions: fuzzy membership aggregation, neutrosophic projections, bidirectional methods.
How PapersFlow Helps You Research TOPSIS Method in Risk Evaluation
Discover & Search
Research Agent uses searchPapers('TOPSIS risk evaluation FMEA fuzzy') to retrieve 20+ papers including Zhou et al. (2022) on hesitant fuzzy FMEA-TOPSIS; citationGraph traces extensions from foundational Yang and Wu (2012); findSimilarPapers expands to neutrosophic variants; exaSearch uncovers supply chain applications.
Analyze & Verify
Analysis Agent applies readPaperContent on Zhou et al. (2022) to extract TOPSIS closeness coefficients; verifyResponse with CoVe cross-checks entropy weights against Yang and Wu (2012); runPythonAnalysis recreates TOPSIS rankings via NumPy pandas sandbox with GRADE scoring for methodological rigor.
Synthesize & Write
Synthesis Agent detects gaps in fuzzy TOPSIS scalability via contradiction flagging across Wei et al. (2019) and Zhou et al. (2022); Writing Agent uses latexEditText for risk matrix tables, latexSyncCitations for 10+ references, latexCompile for full report, exportMermaid for decision hierarchy diagrams.
Use Cases
"Reproduce TOPSIS risk ranking from Zhou et al. 2022 power transformer paper using Python."
Research Agent → searchPapers → readPaperContent (Zhou 2022) → Analysis Agent → runPythonAnalysis (pandas NumPy TOPSIS implementation with entropy weights) → matplotlib closeness coefficient plot and ranked CSV output.
"Write LaTeX appendix comparing fuzzy TOPSIS in mining risks from Yang 2012 and Bao 2017."
Research Agent → citationGraph (Yang-Wu 2012 connections) → Synthesis Agent → gap detection → Writing Agent → latexEditText (comparison table) → latexSyncCitations (auto-insert Bao et al. 2017) → latexCompile → PDF with risk ranking matrices.
"Find GitHub repos implementing hybrid FMEA-TOPSIS from recent papers."
Research Agent → searchPapers('FMEA TOPSIS code') → paperExtractUrls (Zhou 2022 supplements) → Code Discovery → paperFindGithubRepo → githubRepoInspect → verified TOPSIS Python notebooks with fuzzy extensions.
Automated Workflows
Deep Research workflow conducts systematic review: searchPapers(50+ TOPSIS risk papers) → citationGraph clustering → DeepScan 7-step analysis with GRADE checkpoints on Zhou et al. (2022) vs. Yang and Wu (2012). Theorizer generates hybrid entropy-fuzzy TOPSIS theory from Guo et al. (2020) and Wei et al. (2021). Chain-of-Verification validates rankings across datasets.
Frequently Asked Questions
What defines TOPSIS in risk evaluation?
TOPSIS ranks alternatives by Euclidean distance to positive-ideal (best) and negative-ideal (worst) solutions across normalized criteria, with closeness coefficient determining priority.
What are common methods extending TOPSIS for risks?
Extensions include entropy weighting (Yang and Wu, 2012), hesitant fuzzy sets (Zhou et al., 2022), neutrosophic linguistic sets (Wei et al., 2021), and hybrid FMEA integration (Bao et al., 2017).
What are key papers on TOPSIS risk evaluation?
Zhou et al. (2022, 66 citations) on power transformers; Yang and Wu (2012) on coal outbursts; Guo et al. (2020, 32 citations) on electricity retail; foundational Sharp (2009, 109 citations) fuzzy AHP context.
What open problems exist in TOPSIS risk models?
Challenges include real-time scalability for dynamic risks, consistent weight fusion in group settings, and validation of fuzzy extensions against empirical failures.
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