A hybrid multi-objective shuffled frog-leaping algorithm for a mixed-model assembly line sequencing problem |
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| Affiliation: | 1. Department of Industrial Engineering, University of Tehran, P.O. Box: 11365/4563, Tehran, Iran;2. Department of Industrial Engineering, Tarbiat Modares University, P.O. Box: 14115/111, Tehran, Iran;1. Moscow Institute of Physics and Technology, Dolgoprudny, Russia;2. Inria, Grenoble, France;1. Green Technology Research Group, Facultad de Ingeniería y Ciencias Aplicadas, Universidad de los Andes, Chile, Mons. Álvaro del Portillo 12455, Las Condes, Santiago 7620001, Chile;2. School of Biochemical Engineering, Pontificia Universidad Católica de Valparaíso, Av. Brasil 2085, Valparaíso, Chile;3. Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB2 3RA, UK;1. School of Mechano-Electronic Engineering, Xidian University, Xi’an 710071, China;2. School of Economics and Management, Xidian University, Xi’an 710071, China;3. Center for Polymer Studies and Department of Physics, Boston University, Boston, MA 02215, USA;4. Key Laboratory of Electronic Equipment Structure Design, Ministry of Education, Xidian University, Xi’an 710071, China;5. School of Economics and Management, Southwest Jiaotong University, Chengdu 610031, China |
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| Abstract: | In this paper, a mixed-model assembly line (MMAL) sequencing problem is studied. This type of production system is used to manufacture multiple products along a single assembly line while maintaining the least possible inventories. With the growth in customers’ demand diversification, mixed-model assembly lines have gained increasing importance in the field of management. Among the available criteria used to judge a sequence in MMAL, the following three are taken into account: the minimization of total utility work, total production rate variation, and total setup cost. Due to the complexity of the problem, it is very difficult to obtain optimum solution for this kind of problems by means of traditional approaches. Therefore, a hybrid multi-objective algorithm based on shuffled frog-leaping algorithm (SFLA) and bacteria optimization (BO) are deployed. The performance of the proposed hybrid algorithm is then compared with three well-known genetic algorithms, i.e. PS-NC GA, NSGA-II, and SPEA-II. The computational results show that the proposed hybrid algorithm outperforms the existing genetic algorithms, significantly in large-sized problems. |
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