Nowadays, manufacturing enterprises, as larger energy consumers, face the severe environmental challenge and the mission of reducing energy consumption. Therefore, how to reduce energy consumption becomes a burning issue for manufacturing. Production scheduling provides a feasible scheme for energy saving on the system level. However, the existing researches of energy-saving scheduling rarely focus on the permutation flow line scheduling problem. This article proposes an energy-saving method for permutation flow line scheduling problem. First, a mathematical model for the permutation flow line scheduling problem is developed based on the principle of multiple energy source system of the computer numerical control machine tool. The optimization objective of this model is to simultaneously minimize the total flowtime and the fixed energy consumption. Since permutation flow line scheduling problem is a well-known NPhard problem, the non-dominated sorting genetic algorithm II is adopted to solve the multi-objective permutation flow line scheduling problem. Finally, the effectiveness of this method is verified by numerical illustration. The computation results show that a significant trade-off between total flowtime and fixed energy consumption for the permutation flow line scheduling problem, and there would be potential for saving energy consumption by using the proposed method.
The real-time acquisition of machining task progress is one of the most important tasks of production management and is an essential aspect of manufacturing information. Targeted to the machining mode of mixed-category workpieces in job shops, a method for the acquisition of real-time machining task progress is proposed. The method is based on both the power feature of workpiece machining and the incremental learning Lagrangian support vector machine. First, the framework for this method is presented in a straightforward manner. Second, by analysing the characteristics of power change during the machining process, the power feature vector, which reflects the characteristics of workpiece machining, is designed for Lagrangian support vector machine. Then, based on the principle of incremental learning Lagrangian support vector machine, which can address the classification of mixed-category workpieces and the problem of an insufficient number of training samples for training the initial classifiers during the actual machining process, a detailed application of this method is constructed for workpiece classification and the acquisition of machining task progress. Finally, the effectiveness of this method is empirically tested by application to a case study.
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