Discovering task assignment rules for assembly line balancing via genetic programming

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@Article{baykasoglu:2015:IJAMT,

• author = "Adil Baykasoglu and Lale Ozbakir",
• title = "Discovering task assignment rules for assembly line balancing via genetic programming",
• journal = "The International Journal of Advanced Manufacturing Technology",
• year = "2015",
• volume = "76",
• number = "1-4",
• pages = "417--434",
• keywords = "genetic algorithms, genetic programming, Assembly line balancing, Automatic rule generation, Evolutionary intelligence",
• URL = "http://link.springer.com/article/10.1007/s00170-014-6295-4",
• DOI = "doi:10.1007/s00170-014-6295-4",
• size = "18 pages",
• abstract = "Assembly line is one of the most commonly used manufacturing processes to produce final products in a flow line. Design of efficient assembly lines has considerable importance for the production of high-quantity standardized products. Several solution approaches such as exact, heuristic, and metaheuristics have been developed since the problem is first formulated. In this study, a new approach based on genetic programming so as to generate composite task assignment rules is proposed for balancing simple assembly lines. The proposed approach can also be applied to other types of line balancing problems. The present method makes use of genetic programming to discover task assignment rules which can be used within a single-pass constructive heuristic in order to balance a given assembly line quickly and effectively. Suitable parameters affecting the balance of the assembly line are evaluated and employed to discover highly efficient composite task assignment rules. Extensive computational results and comparisons proved the efficiency of the proposed approach in producing generic composite task assignment rules for balancing assembly lines.",

}

Genetic Programming entries for Adil Baykasoglu Lale Ozbakir

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