dc.contributor.author | Kucukkoc, Ibrahim | |
dc.contributor.author | Zhang, David Z. | |
dc.date.accessioned | 2015-06-17T10:40:13Z | |
dc.date.issued | 2015-06-10 | |
dc.description.abstract | 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 t test. 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. | en_GB |
dc.identifier.citation | Volume 82, Issue 1, pp 265-285 | |
dc.identifier.doi | 10.1007/s00170-015-7320-y | |
dc.identifier.uri | http://hdl.handle.net/10871/17584 | |
dc.language.iso | en | en_GB |
dc.publisher | Springer | en_GB |
dc.rights.embargoreason | Publisher policy | en_GB |
dc.rights | This 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. | en_GB |
dc.subject | Assembly line balancing | en_GB |
dc.subject | Model sequencing | en_GB |
dc.subject | Mixed model parallel two-sided assembly lines | en_GB |
dc.subject | Agent-based ant colony optimization | en_GB |
dc.subject | Genetic algorithm | en_GB |
dc.subject | Artificial intelligence | en_GB |
dc.title | Integrating ant colony and genetic algorithms in the balancing and scheduling of complex assembly lines | en_GB |
dc.type | Article | en_GB |
dc.identifier.issn | 0268-3768 | |
dc.description | Copyright © 2015 Springer. This is a PDF file of an unedited manuscript that has been accepted for publication in The International Journal of Advanced Manufacturing Technology. The final publication is available at: http://link.springer.com/article/10.1007/s00170-015-7320-y. 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. | en_GB |
dc.identifier.eissn | 1433-3015 | |
dc.identifier.journal | International Journal of Advanced Manufacturing Technology | en_GB |