A review of the current applications of genetic algorithms in assembly line balancing

Taşan, Seren Özmehmet; Tunalı, Semra

doi:10.1007/s10845-007-0045-5

Cited by 141 publications

(80 citation statements)

References 50 publications

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“…Metaheuristics are also more flexible than exact methods as they are problem-independent solution algorithms which can be adapted to fit the needs of most real-life optimization problems; see, for example, Tiwari and Vidyarthi (2000), Tasan and Tunali (2008), and Pratap et al (2015).…”

Section: Proposed Methodsmentioning

confidence: 99%

Mixed-model parallel two-sided assembly line balancing problem: A flexible agent-based ant colony optimization approach

Küçükkoç

Zhang

2016

Computers & Industrial Engineering

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Assembly lines are frequently used as a production method to assemble complex products. Two-sided assembly lines are utilized to assemble large-sized products (e.g., cars, buses, trucks). Locating two lines in parallel helps improve line efficiency by enabling collaboration between the line workers. This paper proposes a mixed-model parallel twosided assembly line system that can be utilized to produce large-sized items in an inter-mixed sequence. The mixed-model parallel two-sided line balancing problem is defined and the advantages of utilizing multi-line stations across the lines are discussed. A flexible agent-based ant colony optimization algorithm is developed to solve the problem and a numerical example is given to explain the method systematically. The proposed algorithm builds flexible balancing solutions suitable for any model sequence launched. The dynamically changing workloads of workstations (based on specific product models during the production process) are also explored. A comprehensive experimental study is conducted and the results are statistically analyzed using the well-known paired sample t-test. The test results indicate that the mixed-model parallel two-sided assembly line system reduces the workforce need in comparison with separately balanced mixed-model two-sided lines. It is also shown that the proposed algorithm outperforms the tabu search algorithm and six heuristics often used in the assembly line balancing domain.  Section 3 has been re-written in elucidate manner. More explanations on acronyms used in Section 5.1 have been added for clarification. The manuscript has been carefully reviewed by an experienced proof-reader whose first language is English and who specializes in reviewing papers written by authors whose native language is not English.Our detailed responses to reviewer's comments have also been attached to this document.We hope that these revisions will be sufficient to make our manuscript suitable for publication in 'Computers & Industrial Engineering'. We believe that this study will become a base point for further works and yield more researches in this domain by attracting further interests from both academia and industry.We shall look forward to hearing from you at your earliest convenience. AbstractAssembly lines are frequently used as a production method to assemble complex products. Two-sided assembly lines are utilized to assemble large-sized products (e.g., Mixed-model Parallel Detailed Response to ReviewersOverall:The Authors have addressed all comments and queries, but still the paper needs some minor improvements. Response:Your comments are appreciated. All comments have been addressed individually and the blue font has been used in the revised paper to highlight the changes performed. Please find below the detailed responses to your comments. Comment-1:In proposed methodology, authors mentioned that heuristic and metaheuristic is used when it is hard to get solution, what does it mean? Response-1: Thank you for your comment. The sentence ...

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Section: Proposed Methodsmentioning

confidence: 99%

Mixed-model parallel two-sided assembly line balancing problem: A flexible agent-based ant colony optimization approach

Küçükkoç

Zhang

2016

Computers & Industrial Engineering

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“…Phase 4 (Checking to finish) [7] Step 7: if the best solution is not reached go to step 2. There are 3 most important phases to do for 2DCAHGA and at the end of cellular genetic phase, there is a new population on cells.…”

Section: Steps Of 2dcahgamentioning

confidence: 99%

“…Resource distribution [2], management [3] and Assignment: Channel Assignment Problem [4], Graph Coloring Problem [5]; Multi-objective Optimization of Systems: 0/1 Multiple Knapsack Problem [6], Assembly Line Balancing Problem [7], etc. are considered to solve by GA.…”

Section: Introductionmentioning

confidence: 99%

Cellular-based Population to Enhance Genetic Algorithm for Assignment Problems

Cheshmehgaz¹,

Haron²,

Maybodi³

2012

AJIS

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In this paper, we describe a new mechanism of cellular selection as an improved Genetic Algorithm for some optimization problems like Cellular Channel assignment, which have multi feasible/optimum solution per one case. Considering the problems and the nature of relationship among individuals in population, we use 2-dimension Cellular Automata in order to place the individuals onto its cells to make the locality and neighborhood on the Hamming distance basis. This idea as 2D Cellular Automata Hamming GA has introduced locality in Genetic Algorithms and global knowledge for their selection process on Cells of 2D Cellular Automata. The selection based on 2D Cellular Automata can ensure maintaining population diversity and fast convergence in the genetic search. The cellular selection of individuals is controlled based on the structure of cellular automata, to prevent the fast population diversity loss and improve the convergence performance during the genetic search.

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“…If the bottleneck station is automatic station, it cannot be changed because of the high cost of changing automatic equipment, so the cycle time (CT) of the assembly line is not changed. In this case, redistributing work elements and reducing manual station can achieve the balance [1]. This situation is studied in this paper.…”

Section: Introductionmentioning

confidence: 99%

Assembly line balancing based on a genetic algorithm

Lin¹,

Zhang²

2015

Proceedings of the 2015 International Conference on Advanced Engineering Materials and Technology

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Abstract. Assembly line balancing research is a key for the modern production. The implementation steps of genetic algorithm are proposed in this paper and it is used to optimize the assembly line balancing problems in engineering practice. The genetic algorithm operators based on feasible operating sequences of these job orders, which is guarantee the feasibility of the solution, and the optimal retention strategy is used to ensure the effective of the algorithm. Finally the assembly line reduce 2 work stations after calculation, and it makes each station load become more uniform.

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A review of the current applications of genetic algorithms in assembly line balancing

Cited by 141 publications

References 50 publications

Mixed-model parallel two-sided assembly line balancing problem: A flexible agent-based ant colony optimization approach

Mixed-model parallel two-sided assembly line balancing problem: A flexible agent-based ant colony optimization approach

Cellular-based Population to Enhance Genetic Algorithm for Assignment Problems

Assembly line balancing based on a genetic algorithm

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