Solving binary-state multi-objective reliability redundancy allocation series-parallel problem using efficient epsilon-constraint, multi-start partial bound enumeration algorithm, and DEA
In this paper, a procedure based on efficient epsilon-constraint method and data envelopment analysis (DEA) is proposed for solving binary-state multi-objective reliability redundancy allocation series-parallel problem (MORAP). In first module, a set of qualified non-dominated solutions on Pareto fr...
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| Veröffentlicht in: | Reliability engineering & system safety Jg. 103; S. 35 - 44 |
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01.07.2012
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| Abstract | In this paper, a procedure based on efficient epsilon-constraint method and data envelopment analysis (DEA) is proposed for solving binary-state multi-objective reliability redundancy allocation series-parallel problem (MORAP). In first module, a set of qualified non-dominated solutions on Pareto front of binary-state MORAP is generated using an efficient epsilon-constraint method. In order to test the quality of generated non-dominated solutions in this module, a multi-start partial bound enumeration algorithm is also proposed for MORAP. The performance of both procedures is compared using different metrics on well-known benchmark instance. The statistical analysis represents that not only the proposed efficient epsilon-constraint method outperform the multi-start partial bound enumeration algorithm but also it improves the founded upper bound of benchmark instance. Then, in second module, a DEA model is supplied to prune the generated non-dominated solutions of efficient epsilon-constraint method. This helps reduction of non-dominated solutions in a systematic manner and eases the decision making process for practical implementations.
► A procedure based on efficient epsilon-constraint method and DEA was proposed for solving MORAP. ► The performance of proposed procedure was compared with a multi-start PBEA. ► Methods were statistically compared using multi-objective metrics. |
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| AbstractList | In this paper, a procedure based on efficient epsilon-constraint method and data envelopment analysis (DEA) is proposed for solving binary-state multi-objective reliability redundancy allocation series-parallel problem (MORAP). In first module, a set of qualified non-dominated solutions on Pareto front of binary-state MORAP is generated using an efficient epsilon-constraint method. In order to test the quality of generated non-dominated solutions in this module, a multi-start partial bound enumeration algorithm is also proposed for MORAP. The performance of both procedures is compared using different metrics on well-known benchmark instance. The statistical analysis represents that not only the proposed efficient epsilon-constraint method outperform the multi-start partial bound enumeration algorithm but also it improves the founded upper bound of benchmark instance. Then, in second module, a DEA model is supplied to prune the generated non-dominated solutions of efficient epsilon-constraint method. This helps reduction of non-dominated solutions in a systematic manner and eases the decision making process for practical implementations.
► A procedure based on efficient epsilon-constraint method and DEA was proposed for solving MORAP. ► The performance of proposed procedure was compared with a multi-start PBEA. ► Methods were statistically compared using multi-objective metrics. In this paper, a procedure based on efficient epsilon-constraint method and data envelopment analysis (DEA) is proposed for solving binary-state multi-objective reliability redundancy allocation series-parallel problem (MORAP). In first module, a set of qualified non-dominated solutions on Pareto front of binary-state MORAP is generated using an efficient epsilon-constraint method. In order to test the quality of generated non-dominated solutions in this module, a multi-start partial bound enumeration algorithm is also proposed for MORAP. The performance of both procedures is compared using different metrics on well-known benchmark instance. The statistical analysis represents that not only the proposed efficient epsilon-constraint method outperform the multi-start partial bound enumeration algorithm but also it improves the founded upper bound of benchmark instance. Then, in second module, a DEA model is supplied to prune the generated non-dominated solutions of efficient epsilon-constraint method. This helps reduction of non-dominated solutions in a systematic manner and eases the decision making process for practical implementations. |
| Author | Khalili-Damghani, Kaveh Amiri, Maghsoud |
| Author_xml | – sequence: 1 givenname: Kaveh surname: Khalili-Damghani fullname: Khalili-Damghani, Kaveh email: kaveh.khalili@gmail.com organization: Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran – sequence: 2 givenname: Maghsoud surname: Amiri fullname: Amiri, Maghsoud organization: Industrial Management Department, Management and Accounting Faculty, Allameh Tabataba'i University, Tehran, Iran |
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| Keywords | Pareto front DEA ε-constraint method Multi-objective redundancy allocation problem Performance evaluation Statistical analysis Enumeration Filtering Pareto optimum Decision making Redundancy Multiobjective programming Non parametric method Non parametric estimation Modeling Multi-objective redundancy allocation Upper bound problem Efficiency Metric Reliability Data envelopment analysis |
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| SubjectTerms | Algorithms Allocations Applied sciences Benchmarking DEA Decision theory. Utility theory Enumeration Exact sciences and technology Mathematical models Mathematics Modules Multi-objective redundancy allocation problem Nonparametric inference Operational research and scientific management Operational research. Management science Operations research Pareto front Probability and statistics Redundancy Reliability theory. Replacement problems Sciences and techniques of general use Statistics ε-constraint method |
| Title | Solving binary-state multi-objective reliability redundancy allocation series-parallel problem using efficient epsilon-constraint, multi-start partial bound enumeration algorithm, and DEA |
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