A New Approach for Solving Bi-Objective Redundancy Allocation Problem Using DOE, Simulation and ε-Constraint Method

Keshavarz Ghorabaee, Mehdi; Amiri, Maghsoud; Turskis, Zenonas

doi:10.15388/Informatica.2017.121

Informatica

A New Approach for Solving Bi-Objective Redundancy Allocation Problem Using DOE, Simulation and ε-Constraint Method

Volume 28, Issue 1 (2017), pp. 79–104

Mehdi Keshavarz Ghorabaee Maghsoud Amiri Zenonas Turskis

https://doi.org/10.15388/Informatica.2017.121

Pub. online: 1 January 2017 Type: Research Article

Open Access

Received
1 September 2016

Accepted
1 March 2017

Published
1 January 2017

Abstract

The redundancy allocation problem (RAP) has been studied for many different system structures, objective functions, and distribution assumptions. In this paper, we present a problem formulation and a solution methodology to maximize the system steady-state availability and minimize the system cost for the repairable series-parallel system designs. In the proposed approach, the components’ time-to-failure (TTF) and time-to-repair (TTR) can follow any distribution such as the Gamma, Normal, Weibull, etc. We estimate an approximation of the steady-state availability of each subsystem in the series-parallel system with an individual meta-model. Design of experiment (DOE), simulation and the stepwise regression are used to build these meta-models. Face centred design, which is a type of central composite design is used to design experiments. According to a max–min approach, obtained meta-models are utilized for modelling the problem alongside the cost function of the system. We use the augmented ε-constraint method to reformulate the problem and solve the model. An illustrative example which uses the Gamma distribution for TTF and TTR is explained to represent the performance of the proposed approach. The results of the example show that the proposed approach has a good performance to obtain Pareto (near-Pareto) optimal solutions (system configurations).

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Biographies

Keshavarz Ghorabaee Mehdi

m.keshavarz_gh@yahoo.com

M. Keshavarz Ghorabaee received the BS degree in electrical engineering from University of Guilan, Rasht, Iran in 2010 and the MS degree in production management from Allameh Tabataba’i University, Tehran, Iran in 2013. He is currently working toward the PhD degree in Operations Research at Allameh Tabataba’i University. He has published some papers in leading international journals such as Robotics and Computer-Integrated Manufacturing, The International Journal of Advanced Manufacturing Technology, Journal of Cleaner Production and Applied Mathematical Modelling. His research interests include multi-criteria decision making (MCDM), multi-objective evolutionary algorithms, genetic algorithm, fuzzy MCDM, inventory control, supply chain management, scheduling and reliability engineering.

Amiri Maghsoud

amiri@atu.ac.ir

M. Amiri is a professor at the Department of Industrial Management, Allameh Tabataba’i University, Tehran, Iran. He received PhD degree in industrial engineering from Sharif University of Technology, Tehran, Iran. He has published many papers in leading international journals. His research interests include multi-criteria decision-making (MCDM), data envelopment analysis (DEA), design of experiments (DOE), response surface methodology (RSM), fuzzy MCDM, inventory control, supply chain management, simulation and reliability engineering.

Turskis Zenonas

zenonas.turskis@vgtu.lt

Z. Turskis is Prof. Dr. of Technical Sciences, Chief Research Fellow at the Laboratory of Construction Technology and Management, Vilnius Gediminas Technical University. Research interests: building technology and management, decision-making theory, computer-aided automation in design, expert systems. He is the author of more than 110 research papers, which are referred in the WoS database.

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redundancy allocation problem bi-objective RAP design of experiment simulation ε-constraint method

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