Multicriteria and Optimization Models for Risk, Reliability, and Maintenance Decision Analysis: Recent Advances

Contents
Part I New Developments on Building MCDM/A Models
Multicriteria Decision Methods for RRM Models
1 Introduction
2 MCDM/A Models
3 Multicriteria Decision Methods
3.1 Additive Aggregation Methods for Deterministic Situations
3.2 Additive Aggregation Methods for Probabilistic Situations
3.2.1 Expected Utility Theory
3.2.2 Multi-Attribute Utility Theory
3.3 Outranking Methods
3.3.1 ELECTRE Methods
3.3.2 PROMETHEE Methods
3.4 Other MCDM/A Methods
4 Challenges and Future Developments in MCDM/A for RRM
4.1 Partial Information Methods in MCDM/A
4.2 Decision Under Uncertainty: Rank-Dependent Utility and Prospect Theory
4.2.1 Rank-Dependent Utility (RDU)
4.2.2 Prospect Theory
References
Comparing Cardinal and Ordinal Ranking in MCDM Methods
1 Introduction
2 Surrogate Weight Methods
2.1 Strength of Weights
2.2 Preference Strength Methods
3 Assessment of Models for Weights
3.1 Comparing Six MCDA Methods
3.2 Measurements
4 Concluding Remarks
References
Evaluating Multi-criteria Decisions Under Conditions of Strong Uncertainty
1 Introduction
2 Probabilistic Approaches
3 Multi-criteria Decision Trees
4 Probabilistic Multi-criteria Hierarchies
5 Strong Uncertainty
6 Beliefs in Intervals
7 Evaluation Steps
8 Real-Life Decision Example
9 Concluding Remarks
References
A Framework for Building Multicriteria Decision Models with Regard to Reliability, Risk, and Maintenance
1 Introduction
2 Building Multicriteria Decision Models
3 A Framework for Building Multicriteria Models in RRM
3.1 Step 1 – Identify DM and Other Actors in the Decision-Making Process
3.2 Step 2 – Identify Objectives
3.3 Step 3 – Define Family of Criteria
3.4 Step 4 –Establish Alternatives
3.5 Step 5 – Define State of Nature
3.6 Step 6 – Establish a Priori Probability
3.7 Step 7 – Establish Consequence Function
3.8 Step 8 – Preference Modeling
3.9 Step 9 – Conducting an Intra-Criterion Evaluation
3.10 Step 10 – Conducting an Inter-Criteria Evaluation
3.11 Step 11 – Evaluate Alternatives to Find a Solution
3.12 Step 12 – Conduct a Sensitivity Analysis
3.13 Step 13 – Draw Up Recommendation
3.14 Step 14 – Implement the Solution
4 Conclusions
References
Part II MCDM/A Models for Risk Decision Analysis
A Participatory MCDA Approach to Energy Transition PolicyFormation
1 Energy Transition
2 Criteria and Stakeholders
2.1 Policymakers
2.2 Finance and Industry
2.3 Academia
2.4 Young Leaders
2.5 Civil Society and NGOs
2.6 Local Communities
3 Criteria Ranking
3.1 Ranking of Different Criteria
3.2 Procedural and Output Justice
4 Decision Evaluation
4.1 Encoding of Criteria Weights
4.2 Aggregating the Components
5 Trade-Offs Between Technologies
6 Conclusions
References
A Proposition of a Multidimensional HAZOP Analysis (MHAZOP) to Support a Decision-Making Process
1 Introduction
2 HAZOP Study
3 MCDM Approach
4 Multidimensional HAZOP Analysis
4.1 Identifying the Decision Maker (DM)
4.2 Definition HAZOP Phase: Define the Scope, Objectives and Select Multidisciplinary Team
4.3 HAZOP Preparation Phase: Plan, Estimate Time Required, Arrange the Schedule, and Collect Data
4.4 Define Limits of the Study in the System/Equipment/Process
4.5 Selecting Nodes (i1,i2,…,in) and Identifying the Parameters of the Process (j1,j2,…,jm)
4.6 Identifying Alternatives (Deviations)
4.6.1 Combining the Guidewords (g1,g2,…,gp) and Parameters j =→ Deviations: d(i,j,g)
4.6.2 Identify New Critical Combinations of Guide-Words and Parameters: d(i,j = j,g = g)
4.7 Identify Hazard Scenarios (1,2,…,q)
4.8 Conduct an Exposure Analysis of Objects Due to the Hazard Scenario Occurring in the Deviations d(i,j = j,g = g)
4.9 DM's Preference Structure
4.10 Identify Control Measures
4.11 Estimate the Consequences of Probability Functions P(pC"026A30C , d(i,j = j,g = g))
4.12 Estimate the Probabilities of Hazard Scenarios πd()
4.13 Calculate the Loss Function −u[P(p(c)"026A30C , d())]
4.14 Estimate Risk rd(i,j = j,g = g)
4.15 Rank the Risk from All Alternatives rd
4.16 Conduct a Detailed Analysis of the Hierarchy of Risk and Sensitivity Analysis
4.17 Implement Risk Management Actions
4.18 Reporting and Monitoring HAZOP Phase: Record, Sign-Off Records, Produce Report, Monitor and Review Risk and Review Documentation
5 Final Remarks
References
Multidimensional Risk Evaluation in Natural Gas Pipelines: Contributions from Sensitivity Analysis and Risk Visualization to Improving the Management of Risk
1 Introduction
2 Evaluating Multidimensional Risk
3 The Importance of Sensitivity Analysis (SA) for Evaluating Risk
3.1 Sensitivity Analysis and Evaluating Multidimensional Risk
3.2 Visualization of Risk for Sensitivity Analysis
References
Multidimensional Decision-Making Process for Managing Flood Risks in Postmodern Cities: Challenges, Trends, and Sharing Insights to Construct Models That Deal with Climate Changes
1 Introduction: Characterizing Flooding in the Urban Context
1.1 Justifying Flood Risk Management (FRM) Practices and Research
1.2 Urbanization Processes: A New Challenge for Postmodern Societies
2 Impact of Flooding on Everyday Life and Climate Change Effects: A Starting Point for Constructing Decision Problems
3 MCDA/M Approach to FRM in Urban Areas
3.1 Multicriteria Aid for Risk Decision Problems: A Brief Description of the State-of-Art
3.2 The Role of Uncertainty in Evaluating Flood Risks
4 A Multimethodology Framework for Multicriteria Assessment of Flood Risk
5 Multicriteria Model for Prioritizing Flood Risks Using Decision Theory and MultiAttribute Utility Theory
6 Open Issues & Insights for Multicriteria Models in FRM context
References
Part III MCDM/A Models for Reliability and Maintenance Decision Analysis
Multicriteria Decision Model to Support Maintenance Planning in Sewage Systems
1 Introduction
2 Motivation
3 Notation
4 The Proposed Mathematical Model
4.1 Model Assumptions
4.2 Model Development
4.2.1 Mathematical Development of the Scenarios
4.2.2 Joining the Scenarios
4.2.3 Calculating the Cost Rate (Cost Criterion)
4.2.4 Calculating the Rate of Unmet Demands (Risk Criterion)
5 The Multicriteria Model
6 Numerical Application
6.1 Specifying the Model Input Parameters
6.2 Results
6.2.1 Assessing the Criteria Separately
6.2.2 Using a Multicriteria Model to Define the Maintenance Policy
6.2.3 Evaluating the Investment in Higher Quality Maintenance
7 Conclusion
References
A Multicriteria Model to Determine Maintenance Policy for a Protection System Subject to Imperfect Maintenance
1 Introduction
2 Problem Statement
3 Notation
4 The Proposed Mathematical Model
4.1 Model Assumptions
4.2 Model Development
4.2.1 Mathematical Development of the Scenarios
4.2.2 Joining the Scenarios
4.2.3 Calculating the Cost Rate (Cost Criterion)
4.2.4 Calculating the Rate of Unmet Demands (Risk Criterion)
5 The Multicriteria Model
6 Numerical Application
6.1 Specifying the Model Input Parameters
6.2 Results and Discussion
6.2.1 Analysis of the Model Sensitivity to Variations in P
6.2.2 Delimitation of the CS to Be Analysed
6.2.3 Intracriterion Evaluation
6.2.4 Intercriteria Evaluation
6.2.5 Sensitivity Analysis
7 Conclusions
References
Multi-criteria Decision Model to Support the Maintenance Policy for Circuit Breakers in an Electrical Substation
1 Introduction
2 A Strategical Problem in Supply Electricity Using Additive-Veto Model
3 Related Works on Circuit Breaker Maintenance
4 Multi-Criteria Decision and Additive-Veto Model
5 Multi-criteria Model Proposed for Maintenance Planning for Circuit Breakers in an Electrical Substation
5.1 Risk Criterion
5.2 Cost Criterion
5.2.1 Veto Definition and Additive Aggregation
5.2.2 Decision-Making Process Using the Proposed Model
6 Case Study Implementation and Discussion of the Results
6.1 Establish the Criteria and the Space of Consequences
6.2 Modeling Preference
6.3 Construction of the Recommendation
6.4 Verification
7 Conclusion
References
Part IV Optimization and Multiobjective Models for Reliability and Safety Models for Systems
A Bayesian Model for Monitoring and Generating Alarms for Deteriorating Systems Working Under Varying Operating Conditions
1 Introduction
2 The Model
2.1 A Deterministic Linear Damage Framework
2.2 A Stochastic Damage Model with a Bayesian Hierarchical Structure
3 Model Training with Data
3.1 A Metropolis-Within-Gibbs (MWG) Approach for Parameter Estimation
3.2 Important Remarks for Parameter Estimation
4 Remaining Useful Life Prediction
5 A Dynamic Policy for Real-Time Alarm Generation
5.1 Special Case—Linear Cost Function and Its Optimal Structure
6 Numerical Experiments
6.1 Simulation Experiments
6.2 A Case Study for the Condition Monitoring of Wind Turbines
6.3 Degradation Monitoring Using the Proposed Model
6.4 Remaining Useful Life Prediction
6.5 The Warning/Alarm Generation Process
7 Conclusion and Future Work
Appendix: Algorithm 1
References
Making Mission Abort Decisions for Systems Operating in Random Environment
Acronyms
Notations
1 Introduction
2 Problem Formulation
3 Mission Success Probability and System Survival Probability
4 Expected Penalty Associated with Uncompleted Mission
5 Multiple Threshold Generalization
6 Further Research
References
Towards Prognostics and Health Management of Multi-Component Systems with Stochastic Dependence
1 Introduction
2 Prognostics for Multi-Component Systems with Stochastic Dependence
2.1 Degradation Model
2.2 Parameter Estimation with Particle Filters
3 Case Study and Health Indicator Extraction
3.1 Case Study
3.2 Health Indicator Extraction for Multi-Component Systems
4 Predicting End of Life of Components
5 Conclusion
References
Multi-objective Bayesian Optimal Design for Accelerated Degradation Testing
1 Introduction
2 IG Process in an ADT and Bayesian Inference
2.1 The Preliminary of IG Process in an ADT
2.2 ADT Settings and Bayesian Inference
3 Multi-objective Bayesian Optimal Model for ADT
3.1 Optimal Objectives
3.2 Optimal Model
3.3 Optimization Procedure for Multi-objective Bayesian Optimal Design of ADT
3.4 Pruning of Pareto Solution by Data Envelopment Analysis
4 Numerical Case of Multi-objective SSADT Bayesian Optimal Model
5 Conclusions
References
Part V Optimization and Multiobjective Models for Maintenance Modelling
Maintenance Requirements Analysis and Whole-Life Costing
1 Introduction: Fundamental Concepts
2 A General Process
3 Practical Application of the General Process
3.1 Investigating an Existing Set of Policies
3.2 Investigating a Known List of Failure Modes
3.3 Application of a Safety Net
4 Challenges in Developing a Maintenance Policy: Failure Analysis
5 Focus on Failure Analysis
6 Views on the State of the Art and New Advances
6.1 Advances in MRA Thinking
6.2 A Word on `Multi-criteria Decision Analysis'
6.3 Has the Time for MRA Passed?
6.4 Identification and Characterisation of Feasible Modes of Failure and Their Behaviour
7 Overall Thoughts
References
Maintenance Policies for Non-repairable Components
1 Introduction
2 Time-Based Maintenance
2.1 Age-Based Maintenance
2.2 Block-Based Maintenance
3 Condition-Based Maintenance
3.1 Delay-Time Model
3.2 Gamma Deterioration Process with Continuous Monitoring
3.3 Gamma Deterioration Process with Periodic Inspections
3.4 Discretizing Continuous-Time Continuous-State Deterioration Processes
3.5 Aperiodic Inspections
4 Concluding Remarks
References
Models of Imperfect Repair
1 Introduction
2 Existing Models of Imperfect Repair Models
2.1 Geometric Process and Its Extensions
2.2 Reduction of Intensity Models
2.3 Reduction of Age Models
2.4 Virtual Component Models
3 Conclusions and Future Development
References
Opportunistic Maintenance Policies for Multi-Components Systems
1 Introduction
2 System Modelling and Maintenance Costs Structures
2.1 Reliability Metrics
2.2 Maintenance Actions and Costs
2.3 Dependence Modeling and Cost Saving
3 Opportunistic Condition-Based Maintenance Policies
3.1 Degradation-Based Opportunistic Maintenance Policy
3.2 Predictive Reliability-Based Opportunistic Maintenance Policy
3.3 Cost Model for Optimization of Maintenance Policies
4 Numerical Example
4.1 Optimum Maintenance Policy
4.2 Impact of Economic Dependence on the Cost
4.3 Impact of Structural Dependence on the Cost
4.4 Impacts of Stochastic Dependence on the Cost
5 Summary and Conclusions
Appendix A: Gamma Distribution
References
Optimal Management of the Flow of Parts for Gas Turbines Maintenance by Reinforcement Learning and Artificial NeuralNetworks
Symbols and Acronyms
1 Introduction
2 Problem Setting
3 Algorithm
4 Case Study
4.1 Case Study Description
4.2 Results Discussion
4.3 Comparison of the Proposed Training Approaches
5 Conclusions
References
Joint Planning of Maintenance and Spare Parts Provision for Industrial Plant
1 Introduction
2 Industrial Context
3 Maintenance Strategies
3.1 Delay-Time Modelling (DTM)
4 Inventory Control Strategies
5 Joint Optimisation for a Single Machine
6 Joint Optimisation for Parallel Machines
7 Conclusions
References
Some New Advances in Modeling for Performance-Based Maintenance Services
1 Introduction
2 Overview of Performance-Based Maintenance
2.1 The Evolution of Maintenance Strategy
2.2 Implementation of Performance-Based Maintenance
3 Identifying and Defining Performance Measures
3.1 Five Key Performance Measures
3.2 Operational Availability Under Corrective Replacement
3.3 Operational Availability Under Age-Based Replacement
4 Contracting for Profit Maximization
4.1 System Life Cycle Cost
4.2 Incentive Payment Model
4.3 Service Profit Maximization
4.4 Application to Wind Power Industry
5 Contracting Under Condition-Based Maintenance
5.1 Maintenance Optimization Model
5.2 System Availability and Cost Rate
5.3 Numerical Experiments
6 Future Research Direction
6.1 Maintenance Planning Under a Variable System Fleet
6.2 Performance-Based Maintenance in Post-Warranty Service
7 Conclusions
References
Selective Maintenance Optimization Under Uncertainties
1 Introduction
2 Basic Selective Maintenance Model and Uncertainties
2.1 Basic Selective Maintenance Model
2.2 Uncertainties in Selective Maintenance
3 Selective Maintenance Models Under Uncertainties
3.1 Model 1: Uncertainty Associated with Maintenance Efficiency
3.2 Model 2: Uncertainty Associated with Maintenance Cost
3.3 Model 3: Uncertainty Associated with the Durations of Missions and Breaks
3.4 Model 4: Uncertainty Associated with Performance Capacities and State Transition Intensities of Components
3.5 Model 5: Uncertainty Associated with the Durations of Breaks and Maintenance Actions
3.5.1 Stochastic Durations of Maintenance Actions and Breaks
3.5.2 Sequence of Maintenance Actions
3.5.3 The Probability Distribution of the Number of Completed Maintenance Actions
3.5.4 The Proposed Selective Maintenance Model
3.5.5 Illustrative Example 1
3.6 Model 6: Uncertainty Associated with Imperfect Observations
3.6.1 State and Effective Age Under Imperfect Observations
3.6.2 State and Effective Age of a Component After Maintenance
3.6.3 Probability of a System Successfully Completing the Next Mission
3.6.4 Robust Selective Maintenance Model
3.6.5 Illustrative Example 2
4 Conclusions and Discussions
References