This book provides in-depth guidance on how to use multi-criteria decision analysis methods for risk assessment and risk management. The frontiers of engineering operations management methods for identifying the risks, investigating their roles, analyzing the complex cause-effect relationships, and proposing countermeasures for risk mitigation are presented in this book.
There is a total of ten chapters, mainly including the indicators and organizational models for risk assessment, the integrated Bayesian Best-Worst method and classifiable TOPSIS model for risk assessment, new risk prioritization model, fuzzy risk assessment under uncertainties, assessment of COVID-19 transmission risk based on fuzzy inference system, risk assessment and mitigation based on simulation output analysis, energy supply risk analysis, risk assessment and management in cash-in-transit vehicle routing problems, and sustainability risks of resource-exhausted cities.
The most significant feature of this book is that it provides various systematic multi-criteria decision analysis methods for risk assessment and management, and illustrates the application of these methods in different fields. This book is beneficial to policymakers, decision-makers, experts, researchers and students related to risk assessment and management.
Author(s): Jingzheng Ren
Series: Industrial Ecology & Environmental Management, 1
Year: 2021
Language: English
Pages: 252
City: Cham
Contents
Contributors
1 Risk Assessment: Indicators and Organizational Models
1.1 Introduction
1.2 Risk Definition
1.3 Risk Opportunity
1.4 International Standard ISO 31,000
1.5 Risk Management Process
1.6 Organizational Methods and Models for Risk Assessment
1.7 Defining Indicators for Risk Assessment in the Manufacturing Industry
1.8 Case Study
1.9 Conclusions
Appendix A
References
2 An Integrated Bayesian BWM and Classifiable TOPSIS Model for Risk Assessment
2.1 Introduction
2.2 Literature Review
2.2.1 FMEA and MCDM
2.2.2 BWM
2.3 The Proposed Risk Assessment Model
2.3.1 Bayesian BWM
2.3.2 Classifiable TOPSIS Technique
2.4 A Real-World Numerical Application
2.4.1 Problem Description
2.4.2 Applies Bayesian BWM to Obtain Risk Factor Weights
2.4.3 Using Bayesian BWM to Evaluate the Risk Scores of Failure Modes
2.4.4 Employs Classifiable TOPSIS to Rank Critical Failure Modes
2.5 Discussion and Conclusions
References
3 A Modified Risk Prioritization Approach Using Best–Worst Method
3.1 Introduction
3.2 Literature Review
3.3 Methods
3.3.1 MCDM for Risk Assessment
3.3.2 FMEA
3.3.3 BWM
3.3.4 Proposed Framework
3.4 Case Study
3.4.1 Observed Manufacturing Plant
3.4.2 Application of the Proposed Framework
3.4.3 Analysis of the Result
3.5 Discussion
3.6 Conclusion
References
4 Fuzzy Risk Assessment in the Presence of Uncertainties in Heterogeneous Preferences Elicitation and Reliability
4.1 Introduction
4.2 Literature Review
4.2.1 Z-Number
4.2.2 Grey Number
4.3 Method
4.3.1 Phase 1: Consensus Reaching
4.3.2 Phase 2: Conversion
4.3.3 Phase 3: Risk Assessment Evaluation
4.4 Theoretical Validation
4.5 Case Study: Fuzzy Risk Assessment in Electrical Arc Welding
4.6 Discussion
4.7 Conclusion
References
5 Assessment of COVID-19 Transmission Risk Through Fuzzy Inference System; an Application for Mining Activities
5.1 Introduction
5.2 Literature Review
5.3 Methods
5.4 Case Study
5.5 Results
5.6 Discussion
5.7 Conclusion
References
6 Simulation Output Analysis for Risk Assessment and Mitigation
6.1 Introduction
6.2 Literature Review
6.3 Systems Simulation for Risk Assessment and Mitigation
6.3.1 Systems Simulation
6.3.2 Steps Suggested for a Study Using Stochastic Simulation
6.3.3 Available Software for Stochastic Simulation
6.4 Output Analysis for Transient Simulations
6.4.1 A Simple Inventory Model
6.4.2 Properties of a Good Estimator
6.4.3 Estimation of Expected Values
6.4.4 Estimation of the Variance and Nonlinear Functions of Expectations
6.4.5 Parameter Uncertainty and Bayesian Estimation
6.4.6 Estimation of Risk Measures Based on Quantiles and M Estimators
6.5 Output Analysis for Steady-State Simulations
6.5.1 Risk Measures for Steady-State Simulations
6.5.2 Estimation of Performance Measures for Steady-State Simulations
6.5.3 Warming Up
References
7 Cause-Effect Analysis, Barriers Identification, and Policy Implications for China’s Energy Security
7.1 Introduction
7.2 Methods
7.2.1 Basic Procedure of FCM
7.2.2 Intuitionistic Fuzzy Set Theory
7.2.3 Intuitionistic FCM
7.3 Factors Identification
7.4 Scenario Simulations
7.4.1 Construction of IFCM
7.4.2 Scenario Descriptions
7.4.3 Simulation Results
7.5 Conclusions and Policy Implications
References
8 Quantitative Risk Assessment and Management of Cash-In-Transit Vehicle Routing Problems
8.1 Introduction
8.2 Problem Description
8.2.1 CIT Business in China
8.2.2 Description of CTVRP
8.3 Methods of Risk Assessment
8.3.1 Distance-Based Methods
8.3.2 Probability-Based Methods
8.3.3 Dissimilarity-Based Methods
8.3.4 Other Methods
8.4 A Demo Model of CTVRP
8.4.1 The CTVRP Model with Risk Constraints
8.4.2 Solution Algorithms of CTVRP
8.5 Discussions
8.6 Conclusions
References
9 Mitigating Energy Supply Risks: Factors Identification and Pathway Selection for China’s Renewable Energy Development Fuzzy PROMETHEE
9.1 Introduction
9.2 Potential Risks Affecting Renewable Energy Development
9.2.1 Technological Risks
9.2.2 Economic Risks
9.2.3 Social-Political Risks
9.2.4 Environmental Risks
9.2.5 A General Framework of Risks Affecting Renewable Energy Development
9.3 Method
9.3.1 Fuzzy Set and Fuzzy Numbers
9.3.2 The Method of PROMETHEE
9.3.3 Procedure of Fuzzy PROMETHEE
9.4 Results and Discussions
9.5 Conclusions
References
10 Sustainability Risks of Resource-Exhausted Cities in China: A Principal Component Analysis
10.1 Introduction
10.2 Methodology and Data
10.2.1 The Principal Component Analysis
10.2.2 Sample Cities and Data
10.3 Structure Detection of Dataset
10.4 The Weights and Sustainability Performance Based on the Principal Component Analysis
10.5 Conclusions and Policies for Mitigating Sustainability Risks
10.6 Code Availability
References
Index
