arrow
Return

Solving binary-state multi-objective reliability redundancy allocation series-parallel problem using efficient epsilon-constraint, multi-start partial bound enumeration algorithm, and DEA

delete2012-07-01
delete52
PRE
AI
K
Kaveh Khalili‐Damghani *
M
Maghsoud Amiri
DOI:10.1016/j.ress.2012.03.006delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
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. (c) 2012 Elsevier Ltd. All rights reserved.
Keywords:
epsilon-constraint method
Pareto front
Multi-objective redundancy allocation problem
DEA

Journal

R
Reliability Engineering and System Safety
IF:
11
Papers:
9.0K
Citations:
4.2W

Organization

I
Islamic Azad University
Scholars:
4.0W
Papers: 3.3W
Citations: 9.8K
A
Allameh Tabataba'i University
Scholars:
537
Papers: 490
Citations: 0
Cited Papers

Cited Papers

errShare
errSave
The Impact of Language Experience on Language and Reading
err2018-01-01
err0
PREAI
errMark S. Seidenberg; Maryellen C. MacDonald
errShare
errSave
researcher View more