Article: Many-objective x-bar control chart using hybrid NSGA-III and DEA Journal: International Journal of Quality Engineering and Technology (IJQET) 2021 Vol.8 No.2 pp.201 - 217 Abstract: The present study aimed to find the optimal design of the x-bar control chart which optimises quadruple objectives. The objectives are the average run length, the detection power, the average time to signal, and the expected cost per hour. In this study, a many-objective mathematical model was developed to find the optimal design and solved it using three different variations of the third version of the non-dominated sorting genetic algorithm, named NSGA-III, θ-NSGA-III, and NSDGA-III. The third one is the contribution of this paper which hybridised the NSGA-III with data envelopment analysis (DEA) and technique for order of preference by similarity to ideal solution (TOPSIS). The performances of the three algorithms were tested using a numerical example. The results revealed that the developed NSDGA-III is well defined to explore the search area and in each generation, it is always the best one in choosing solutions that have higher efficiency. Therefore, it is superior in comparing with other algorithms to find better solutions. Hence, the new hybrid algorithm can outperform NSGA-III and θ-NSGA-III. It can be used in all processes with variation due to an assignable cause. Inderscience Publishers - linking academia, business and industry through research

Title: Many-objective x-bar control chart using hybrid NSGA-III and DEA

Authors: Zahra Mohit-Ghiri; Ali Mohammadi; Seyed-Hadi Mirghaderi

Addresses: Department of Management, School of Economics, Management and Social Sciences, Shiraz University, Shiraz, Iran ' Department of Management, School of Economics, Management and Social Sciences, Shiraz University, Shiraz, Iran ' Department of Management, School of Economics, Management and Social Sciences, Shiraz University, Shiraz, Iran

Abstract: The present study aimed to find the optimal design of the x-bar control chart which optimises quadruple objectives. The objectives are the average run length, the detection power, the average time to signal, and the expected cost per hour. In this study, a many-objective mathematical model was developed to find the optimal design and solved it using three different variations of the third version of the non-dominated sorting genetic algorithm, named NSGA-III, θ-NSGA-III, and NSDGA-III. The third one is the contribution of this paper which hybridised the NSGA-III with data envelopment analysis (DEA) and technique for order of preference by similarity to ideal solution (TOPSIS). The performances of the three algorithms were tested using a numerical example. The results revealed that the developed NSDGA-III is well defined to explore the search area and in each generation, it is always the best one in choosing solutions that have higher efficiency. Therefore, it is superior in comparing with other algorithms to find better solutions. Hence, the new hybrid algorithm can outperform NSGA-III and θ-NSGA-III. It can be used in all processes with variation due to an assignable cause.

Keywords: quality control; x-bar control chart; many-objective; genetic algorithm; multi-objective design; NSGA-III; θ-NSGA-III; NSDGA-III; data envelopment analysis; DEA.

DOI: 10.1504/IJQET.2021.113722

International Journal of Quality Engineering and Technology, 2021 Vol.8 No.2, pp.201 - 217

Received: 20 Dec 2019
Accepted: 19 Jul 2020
Published online: 19 Mar 2021 *

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Title: Many-objective x-bar control chart using hybrid NSGA-III and DEA

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