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An accurate flexible process planning using an adaptive genetic algorithm

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Abstract

The increasing demand for products and services due to globalization has quickly increased through the years. Under such circumstances, the improvement in manufacturing processes has taken the attention of several areas of engineering. Different schemas have been introduced in the context of distributed manufacturing, such as the flexible use of tools, machines, and tool access directions which become a complex task considering the difficult combinatory process and rigorous restrictions. To overcome such complications, flexible process planning (FPP) has been treated as an optimization problem. Moreover, the problem difficulty compromises the proper balance between performance and computational cost which generates the proposal of different optimization techniques, statistical criteria, and hybridizations. Despite the good results of different methods, there are still several possibilities for improvement. In this work, a genetic algorithm (GA) is employed for an accurate FPP process where the GA operators are adapted in order to join up the combinatory optimization process of FPP with the main structure of GA (aGA). To carry out the experimentation, different scenarios of FPP problems using AND/OR networks, production time, and production cost are considered. The adapted genetic algorithm for flexible process planning (aGA-FPP) problems has shown competitive results regarding similar approaches and hybridizations reported in the literature.

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Authors and Affiliations

  1. Departamento de Electrónica, Universidad de Guadalajara, CUCEI, Av. Revolución, 1500, Guadalajara, Jal, México

    Eduardo H. Haro, Omar Avalos, Octavio Camarena & Erik Cuevas

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  1. Eduardo H. Haro
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  2. Omar Avalos
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  3. Octavio Camarena
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  4. Erik Cuevas
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Correspondence to Octavio Camarena.

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Haro, E.H., Avalos, O., Camarena, O. et al. An accurate flexible process planning using an adaptive genetic algorithm. Neural Comput & Applic 35, 6435–6456 (2023). https://doi.org/10.1007/s00521-022-07811-3

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  • DOI: https://doi.org/10.1007/s00521-022-07811-3

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