On Heijunka Design of Assembly Load Balancing Problem: Genetic Algorithm & Ameliorative Procedure-Combined Approach

Zhi Zhuo Hou; Hiroshi Katayama; Reakook Hwang

doi:doi:10.11648/j.ijiis.s.2015040201.17

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On Heijunka Design of Assembly Load Balancing Problem: Genetic Algorithm & Ameliorative Procedure-Combined Approach

Zhi Zhuo Hou, Hiroshi Katayama, Reakook Hwang

Published in International Journal of Intelligent Information Systems (Volume 4, Issue 2-1)

Received: 16 January 2015 Accepted: 19 January 2015 Published: 10 February 2015

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Abstract

Mixed-model straight/U-shaped assembly line has been recognized as a relevant component of Just-In-Time (JIT) production line system. For this system, “Heijunka” design is also challenged as both the task assignment and the production sequence affect the workload imbalance among workstations. In this context and recognizing uncertain task time environment that is often observed in actual manufacturing scene, this research addresses the Line Balancing Problem (LBP) and the Product Sequencing Problem (PSP) jointly and proposes a mathematical model with stochastic task time which is subjected to normal distribution. The objectives of this model are to maximize line efficiency and to minimize the variation of work overload time. A Multi-objective Genetic Algorithm (MOGA) and an Ameliorative Structure of Multi-objective Genetic Algorithm (ASMOGA) with Priority-based Chromosome (PBC) are applied to solve this problem. At last, this research conducts an experimental simulation on a set of benchmark problems to verify the outperformance of the proposed algorithm.

Published in	International Journal of Intelligent Information Systems (Volume 4, Issue 2-1) This article belongs to the Special Issue Logistics Optimization Using Evolutionary Computation Techniques
DOI	10.11648/j.ijiis.s.2015040201.17
Page(s)	49-58
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Mixed-Model Assembly Line, Load Balancing, Multi-Objective Genetic Algorithm, Ameliorative Procedure

References

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[2]	Gen, M. and Cheng, R., Genetic algorithm & engineering design, New York: Wiley, 1997.
[3]	Hwang, R. K. and Katayama, H., “A Multi-decision Genetic Approach for Workload Balancing with Mixed-Model U-shaped Assembly Line Systems”, International Journal of Production Research, Vol. 47, No. 14, pp. 3797-3822, 2009.
[4]	Hwang, R. K., A Study on Load Balancing Problem Solving by Genetic Algorithm -Case Analyses on Assembly Line and Multiprocessor Systems-, Tokyo: Waseda University Doctoral Dissertation, 2009.
[5]	Hwang, R. K. and Katayama, H., “Integrated procedure of balancing and sequencing for mixed-model assembly lines: a multi-objective evolutionary approach”, International Journal of Production Research, Vol. 47, No. 21, pp. 6417-6441, 2010..
[6]	Hackman, S. T., Magazine, M. J. and Wee, T. S., “Fast, Effective Algorithms for Simple Assembly Line Balancing Problems”, Operations Research, Vol. 37 No. 6, pp. 916-924, 1996.
[7]	Kara. Y., Ozcan, U., and Peker, A., “Balancing and Sequencing mixed-model just-in-time U-lines with multiple objectvies”, The International Journal of Advanced Manufacturing Technology, Vol. 32, No. 11-12, pp.1218-1231., 2007.
[8]	Katayama, H., “An integrated management procedure of multi-item mixed-line production system-its hierarchical structure and performance evaluation”, International Journal of Production Research, Vol. 36, No. 10, pp. 2633–2651, 1998.
[9]	Kim, Y. K., Kim, J. Y. and Kim, Y., “An endosymbiotic evolutionary algorithm for the integration of balancing and sequencing in mixed-model U-lines”, European Journal of Operational Research, Vol. 168, No. 3, pp. 838-852, 2006.
[10]	Miltenburg, J., “Balancing and scheduling mixed-model U-shaped production lines”, International Journal of Flexible Manufacturing Systems, Vol. 14, No. 2, pp. 119–151, 2002.
[11]	Scholl, A., Balancing and Sequencing of assembly lines 2nd ed., Hedelberg, Germany: Physisca Press, 1999.
[12]	Ponnambalam S. G., Aravindan P. and Naidu G. M., “Multi-objective genetic algorithm for solving assembly line balancing problem”, International Journal of Advanced Manufacturing Technology, Vol. 16.No. 5, pp. 341-352, 2000.

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APA Style

Zhi Zhuo Hou, Hiroshi Katayama, Reakook Hwang. (2015). On Heijunka Design of Assembly Load Balancing Problem: Genetic Algorithm & Ameliorative Procedure-Combined Approach. International Journal of Intelligent Information Systems, 4(2-1), 49-58. https://doi.org/10.11648/j.ijiis.s.2015040201.17

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ACS Style

Zhi Zhuo Hou; Hiroshi Katayama; Reakook Hwang. On Heijunka Design of Assembly Load Balancing Problem: Genetic Algorithm & Ameliorative Procedure-Combined Approach. Int. J. Intell. Inf. Syst. 2015, 4(2-1), 49-58. doi: 10.11648/j.ijiis.s.2015040201.17

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AMA Style

Zhi Zhuo Hou, Hiroshi Katayama, Reakook Hwang. On Heijunka Design of Assembly Load Balancing Problem: Genetic Algorithm & Ameliorative Procedure-Combined Approach. Int J Intell Inf Syst. 2015;4(2-1):49-58. doi: 10.11648/j.ijiis.s.2015040201.17

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@article{10.11648/j.ijiis.s.2015040201.17,
  author = {Zhi Zhuo Hou and Hiroshi Katayama and Reakook Hwang},
  title = {On Heijunka Design of Assembly Load Balancing Problem: Genetic Algorithm & Ameliorative Procedure-Combined Approach},
  journal = {International Journal of Intelligent Information Systems},
  volume = {4},
  number = {2-1},
  pages = {49-58},
  doi = {10.11648/j.ijiis.s.2015040201.17},
  url = {https://doi.org/10.11648/j.ijiis.s.2015040201.17},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.s.2015040201.17},
  abstract = {Mixed-model straight/U-shaped assembly line has been recognized as a relevant component of Just-In-Time (JIT) production line system. For this system, “Heijunka” design is also challenged as both the task assignment and the production sequence affect the workload imbalance among workstations. In this context and recognizing uncertain task time environment that is often observed in actual manufacturing scene, this research addresses the Line Balancing Problem (LBP) and the Product Sequencing Problem (PSP) jointly and proposes a mathematical model with stochastic task time which is subjected to normal distribution. The objectives of this model are to maximize line efficiency and to minimize the variation of work overload time. A Multi-objective Genetic Algorithm (MOGA) and an Ameliorative Structure of Multi-objective Genetic Algorithm (ASMOGA) with Priority-based Chromosome (PBC) are applied to solve this problem. At last, this research conducts an experimental simulation on a set of benchmark problems to verify the outperformance of the proposed algorithm.},
 year = {2015}
}

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TY  - JOUR
T1  - On Heijunka Design of Assembly Load Balancing Problem: Genetic Algorithm & Ameliorative Procedure-Combined Approach
AU  - Zhi Zhuo Hou
AU  - Hiroshi Katayama
AU  - Reakook Hwang
Y1  - 2015/02/10
PY  - 2015
N1  - https://doi.org/10.11648/j.ijiis.s.2015040201.17
DO  - 10.11648/j.ijiis.s.2015040201.17
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 49
EP  - 58
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.s.2015040201.17
AB  - Mixed-model straight/U-shaped assembly line has been recognized as a relevant component of Just-In-Time (JIT) production line system. For this system, “Heijunka” design is also challenged as both the task assignment and the production sequence affect the workload imbalance among workstations. In this context and recognizing uncertain task time environment that is often observed in actual manufacturing scene, this research addresses the Line Balancing Problem (LBP) and the Product Sequencing Problem (PSP) jointly and proposes a mathematical model with stochastic task time which is subjected to normal distribution. The objectives of this model are to maximize line efficiency and to minimize the variation of work overload time. A Multi-objective Genetic Algorithm (MOGA) and an Ameliorative Structure of Multi-objective Genetic Algorithm (ASMOGA) with Priority-based Chromosome (PBC) are applied to solve this problem. At last, this research conducts an experimental simulation on a set of benchmark problems to verify the outperformance of the proposed algorithm.
VL  - 4
IS  - 2-1
ER  -

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Author Information

Zhi Zhuo Hou

Department of Industrial and Management System Engineering, Graduate School of Creative Science and Engineering, Waseda University, Tokyo, Japan
Hiroshi Katayama

Department of Industrial and Management System Engineering, Faculty of Science and Engineering, Waseda University, Tokyo, Japan
Reakook Hwang

Samsung Economic Research Institute, Seoul, Korea

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Plain Text BibTeX RIS

APA Style

Zhi Zhuo Hou, Hiroshi Katayama, Reakook Hwang. (2015). On Heijunka Design of Assembly Load Balancing Problem: Genetic Algorithm & Ameliorative Procedure-Combined Approach. International Journal of Intelligent Information Systems, 4(2-1), 49-58. https://doi.org/10.11648/j.ijiis.s.2015040201.17

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ACS Style

Zhi Zhuo Hou; Hiroshi Katayama; Reakook Hwang. On Heijunka Design of Assembly Load Balancing Problem: Genetic Algorithm & Ameliorative Procedure-Combined Approach. Int. J. Intell. Inf. Syst. 2015, 4(2-1), 49-58. doi: 10.11648/j.ijiis.s.2015040201.17

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AMA Style

Zhi Zhuo Hou, Hiroshi Katayama, Reakook Hwang. On Heijunka Design of Assembly Load Balancing Problem: Genetic Algorithm & Ameliorative Procedure-Combined Approach. Int J Intell Inf Syst. 2015;4(2-1):49-58. doi: 10.11648/j.ijiis.s.2015040201.17

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@article{10.11648/j.ijiis.s.2015040201.17,
  author = {Zhi Zhuo Hou and Hiroshi Katayama and Reakook Hwang},
  title = {On Heijunka Design of Assembly Load Balancing Problem: Genetic Algorithm & Ameliorative Procedure-Combined Approach},
  journal = {International Journal of Intelligent Information Systems},
  volume = {4},
  number = {2-1},
  pages = {49-58},
  doi = {10.11648/j.ijiis.s.2015040201.17},
  url = {https://doi.org/10.11648/j.ijiis.s.2015040201.17},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.s.2015040201.17},
  abstract = {Mixed-model straight/U-shaped assembly line has been recognized as a relevant component of Just-In-Time (JIT) production line system. For this system, “Heijunka” design is also challenged as both the task assignment and the production sequence affect the workload imbalance among workstations. In this context and recognizing uncertain task time environment that is often observed in actual manufacturing scene, this research addresses the Line Balancing Problem (LBP) and the Product Sequencing Problem (PSP) jointly and proposes a mathematical model with stochastic task time which is subjected to normal distribution. The objectives of this model are to maximize line efficiency and to minimize the variation of work overload time. A Multi-objective Genetic Algorithm (MOGA) and an Ameliorative Structure of Multi-objective Genetic Algorithm (ASMOGA) with Priority-based Chromosome (PBC) are applied to solve this problem. At last, this research conducts an experimental simulation on a set of benchmark problems to verify the outperformance of the proposed algorithm.},
 year = {2015}
}

Copy | Download

TY  - JOUR
T1  - On Heijunka Design of Assembly Load Balancing Problem: Genetic Algorithm & Ameliorative Procedure-Combined Approach
AU  - Zhi Zhuo Hou
AU  - Hiroshi Katayama
AU  - Reakook Hwang
Y1  - 2015/02/10
PY  - 2015
N1  - https://doi.org/10.11648/j.ijiis.s.2015040201.17
DO  - 10.11648/j.ijiis.s.2015040201.17
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 49
EP  - 58
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.s.2015040201.17
AB  - Mixed-model straight/U-shaped assembly line has been recognized as a relevant component of Just-In-Time (JIT) production line system. For this system, “Heijunka” design is also challenged as both the task assignment and the production sequence affect the workload imbalance among workstations. In this context and recognizing uncertain task time environment that is often observed in actual manufacturing scene, this research addresses the Line Balancing Problem (LBP) and the Product Sequencing Problem (PSP) jointly and proposes a mathematical model with stochastic task time which is subjected to normal distribution. The objectives of this model are to maximize line efficiency and to minimize the variation of work overload time. A Multi-objective Genetic Algorithm (MOGA) and an Ameliorative Structure of Multi-objective Genetic Algorithm (ASMOGA) with Priority-based Chromosome (PBC) are applied to solve this problem. At last, this research conducts an experimental simulation on a set of benchmark problems to verify the outperformance of the proposed algorithm.
VL  - 4
IS  - 2-1
ER  -

Copy | Download