Sequencing in mixed model assembly lines: A genetic algorithm approach

Kim, Yeo Keun; Hyun, Chul Ju; Kim, Yeongho

doi:10.1016/s0305-0548(96)00033-0

Cited by 56 publications

(28 citation statements)

References 13 publications

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“…This process continues until all tasks are assigned. [5,4,7], [5,9,10], [8,7,12], [3,6,8], [9,10,13,16], [], [11,12], [11,14,15], []]…”

Section: Simulation Of the Solution Proceduresmentioning

confidence: 99%

“…C A B C F E D [ [1,6,4], [3,2], [1,4,7], [2,5,9], [5,11], [9,7,8], [3,6,12], [10,13,16,8], [], [10,12], [11,15,14], []]…”

mentioning

confidence: 99%

“…C C A B D F E [ [3,1,6,4,7], [2,5], [2,3,6], [1,4,5,7], [], [9,11,12], [8,11,13], [10,14,9], [], [8,10], [16,12,15], []]…”

mentioning

confidence: 99%

“…C C A B F D E [ [1,4,3,6], [2,5,7], [1,4,7], [2,5,10], [9,11], [8,10,12], [3,6,8,16], [9,13] [3,2], [2,3,6], [1,5,4,7], [9,11], [5,8,7,10,15], [8,12,16], [10,9,11,13,14], [], [12],…”

mentioning

confidence: 99%

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Integrating ant colony and genetic algorithms in the balancing and scheduling of complex assembly lines

Küçükkoç

Zhang

2015

Int J Adv Manuf Technol

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Usage guidelinesThis version is made available online in accordance with publisher policies. To see the final version of this paper, please visit the publisher's website (a subscription may be required to access the full text).Before reusing this item please check the rights under which it has been made available. Some items are restricted to non-commercial use. Please cite the published version where applicable. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Different from a large number of existing studies in the literature, this paper addresses two important issues in managing production lines, the problems of line balancing and model sequencing, concurrently. A novel hybrid agent based ant colony optimization -genetic algorithm approach is developed for the solution of mixed-model parallel two-sided assembly line balancing and sequencing problem. The existing agent based ant colony optimization algorithm is enhanced with the integration of a new genetic algorithm based model sequencing mechanism. The algorithm provides ants the opportunity of selecting a random behavior among ten heuristics commonly used in the line balancing domain. A numerical example is given to illustrate the solution building procedure of the algorithm and the evolution of the chromosomes. The performance of the developed algorithm is also assessed through test problems and analysis of their solutions through a statistical test, namely Paired-Sample tTest. In accordance with the test results, it is statistically proven that the integrated genetic algorithm based model sequencing engine helps agent based ant colony optimization algorithm robustly find significantly better quality solutions.

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“…This process continues until all tasks are assigned. [5,4,7], [5,9,10], [8,7,12], [3,6,8], [9,10,13,16], [], [11,12], [11,14,15], []]…”

Section: Simulation Of the Solution Proceduresmentioning

confidence: 99%

“…C A B C F E D [ [1,6,4], [3,2], [1,4,7], [2,5,9], [5,11], [9,7,8], [3,6,12], [10,13,16,8], [], [10,12], [11,15,14], []]…”

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confidence: 99%

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Integrating ant colony and genetic algorithms in the balancing and scheduling of complex assembly lines

Küçükkoç

Zhang

2015

Int J Adv Manuf Technol

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“…However, these two problems were dealt with separately by many researchers (i.e. Askin and Zhou (1997), Gokcen and Erel (1997), Vilarinho and Simaria (2002), McMullen and Tarasewich (2003), Haq et al (2006), Ozcan and Toklu (2009), Hamta et al (2013), , and Manavizadeh et al (2013a) for the line balancing problem; and Yano and Rachamadugu (1991), Bard et al (1992), Kim et al (1996), Zheng et al (2011), Bautista and Cano (2011), and Xu and Li (2013) for the model sequencing problem) with different objectives ever since the mixed-model line balancing problem was first introduced by Thomopoulos (1967).…”

Section: Introductionmentioning

confidence: 99%

Mathematical model and agent based solution approach for the simultaneous balancing and sequencing of mixed-model parallel two-sided assembly lines

Küçükkoç

Zhang

2014

International Journal of Production Economics

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Cite this article as: Ibrahim Kucukkoc, David Z. Zhang, Mathematical model and agent based solution approach for the simultaneous balancing and sequencing of mixed-model parallel two-sided assembly lines, Int. J. Production Economics, http://dx.doi.org/10.1016/j.ijpe. 2014.08.010 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. AbstractOne of the key factors of a successfully implemented mixed-model line system is considering model sequencing problem as well as the line balancing problem. In the literature, there are many studies, which consider these two tightly interrelated problems individually. However, we integrate the model sequencing problem in the line balancing procedure to obtain a more efficient solution for the problem of Simultaneous Balancing and Sequencing of Mixed-Model Parallel Two-Sided AssemblyLines. A mathematical model is developed to present the problem and a novel agent based ant colony optimisation approach is proposed as the solution method. Different agents interact with each other to find a near optimal solution for the problem. Each ant selects a random behaviour from a predefined list of heuristics and builds a solution using this behaviour as a local search rule along with the pheromone value. Different cycle times are allowed for different two-sided lines located in parallel to each other and this yields a complex problem where different production cycles need to be considered to build a feasible solution. The performance of the proposed approach is tested through a set of test cases. Experimental results indicate that considering model sequencing problem with the line balancing problem together helps minimise line length and total number of required workstations. Also, it is found that the proposed approach outperforms other three heuristics tested.Keywords: mixed-model parallel two-sided assembly lines; simultaneous line balancing and model sequencing; agent based ant colony optimisation; production lines; metaheuristics; artificial intelligence. Experimental results indicate that considering model sequencing problem with the line balancing problem together helps minimise line length and total number of required workstations. Also, it is found that the proposed approach outperforms other three heuristics tested.

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