An improved particle swarm optimization model for solving homogeneous discounted series-parallel redundancy allocation problems
Seyed Mohsen Mousavi (mousavi.mohsen8@gmail.com),
Najmeh Alikar (najme.alikar@gmail.com),
Madjid Tavana (tavana@lasalle.edu) and
Debora Di Caprio (dicaper@mathstat.yorku.ca)
Additional contact information
Seyed Mohsen Mousavi: Islamic Azad University
Najmeh Alikar: University of Malaya
Madjid Tavana: La Salle University
Debora Di Caprio: York University
Journal of Intelligent Manufacturing, 2019, vol. 30, issue 3, No 14, 1175-1194
Abstract:
Abstract In this study, we propose an improved particle swarm optimization algorithm (IPSOA) to solve discounted redundancy allocation problems (DRAPs) in series-parallel systems. The system consists of subsystems in series with parallel components in each subsystem. Homogeneous redundant components are used to achieve a desirable system reliability. The components of each subsystem are characterized by their cost, weight, and reliability where the cost is calculated under an all unit discount policy. The goal is to find the optimum combination of the components for each subsystem so that the system reliability is maximized. After formulating the mathematical model, the proposed IPOSA is implemented to achieve the solution. Moreover, an experimental design approach is used to calibrate the algorithm’s parameters. Three numerical problems, each of which considered under several configurations, are discussed to demonstrate the applicability of the proposed procedures. In order to evaluate the accuracy and performance of our IPSOA, all the problems are also solved using two other meta-heuristics, namely, the homogeneous particle swarm optimization algorithm and tabu search. The numerical results show that, when solving DRAPs, IPSOA behaves better than the other two algorithms considered from both a solution quality and a computational viewpoint.
Keywords: Reliability evaluation; Heuristics; Redundancy allocation problem; Improved particle swarm optimization; Series-parallel systems; Hybrid algorithms (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (7)
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DOI: 10.1007/s10845-017-1311-9
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