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Motor manufacturing processes.
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... motor manufacturing processes can be mainly decomposed into several modules as stator & rotor iron core processing, motor parts metal processing, motor axes metal processing, winding equipped stator manufacturing, cage rotor manufacturing and motor assembling & testing. Fig. 1 details the logistic relations between them. The modules outside motor assembling is categorized as parts process module, providing basic parts of motor for ...
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Citations
... Los mejores resultados fueron obtenidos a través del algoritmo ILS, en comparación con los otros cuatro algoritmos. (He et al., 2019) propusieron un modelo distribuido de dos capas (twolayer distributed model, TDM) para el caso de la producción de motores en ambientes de múltiple variedad y lotes pequeños de productos, para optimizar simultáneamente el makespan, la tardanza total y el costo por tiempos de preparación. Los autores abordaron cada capa de trabajo del modelo como un problema de programación de grupos dependientes de la secuencia. ...
El presente libro titulado “Sistemas prácticos para el diseño del layout de plantas industriales con celdas de manufactura” plantea a la comunidad académica y profesional una propuesta de cómo se puede mejorar el desempeño organizacional en los procesos productivos, para lograr mayores eficiencias como producto de una distribución optima de las áreas involucradas en los procesos de manufactura que involucran celdas de producción.
... Besides, customized orders have brought a multi-variety and small-batch manufacturing environment, making the scheduling problem increasingly complex. To this, methods such as simulated annealing and genetic algorithms provide solutions for the production scheduling problem under the background of personalized customization (He, Yang, and Pan 2019;Zandieh 2019). He, Yang, and Pan (2019) proposed simulated annealing adaptive genetic algorithm, which predicts performance based on a two-layer distributed model through data mining. ...
... To this, methods such as simulated annealing and genetic algorithms provide solutions for the production scheduling problem under the background of personalized customization (He, Yang, and Pan 2019;Zandieh 2019). He, Yang, and Pan (2019) proposed simulated annealing adaptive genetic algorithm, which predicts performance based on a two-layer distributed model through data mining. For the virtual cellular manufacturing system (VCMS) scheduling problem, Zandieh (2019) developed a hybrid algorithm based on biogeographic optimization algorithm and genetic algorithm (GA). ...
The data-driven method has been widely used to mine knowledge from the smart shop floors’ production data to guide dynamic scheduling. However, the mined knowledge may be invalid when the production scene changes. In order to address this problem and ensure the validity of the knowledge, this paper studies a data-driven scheduling knowledge life-cycle management (SKLM) method for the smart shop floor. The proposed method includes four phases: knowledge generation, knowledge application, online knowledge evaluation, and knowledge update. Specifically, the extreme learning machine (ELM) is applied to learn knowledge based on the composite scheduling rules. The quality control theory is used to evaluate the quality of scheduling knowledge. And the online sequential ELM (OS-ELM) is adopted to update the knowledge. Knowledge life-cycle management is implemented through the iterative knowledge update. The proposed method is validated on the MIMAC6, which is a simulation model of the semiconductor production line. Experimental results show that the proposed method could improve the effectiveness of scheduling knowledge and further optimize the performance of the smart shop floor.
