Discovery of Key Production Nodes in Multi-objective Job Shop Based on Entropy Weight Fuzzy Comprehensive Evaluation
The multi-objective Job Shop complex network model based on data information is a new idea to solve the transformation of multi-objective shop scheduling problem in recent years. Finding key nodes on the complex networks model is the focus of this paper. The existing key nodes recognition method ignores the overall characteristics of the network, is susceptible to subjective factors, and does not apply to data based complex networks model. According to the characteristics of subjective and objective weighting, the entropy weight method in fuzzy mathematics is applied to the method of analytic hierarchy process (AHP). The next step is to establish a key nodes recognition method suitable for new model–Entropy weight fuzzy comprehensive evaluation method. To some extent, this method has made up for the lack of subjectivity and index capability of the method of analytic hierarchy process. Finally, the simulation results show that the method can effectively mine the key nodes in the model, and prove the rationality and effectiveness of the method.
KeywordsEntropy weight fuzzy comprehensive evaluation Intelligent manufacturing Industrial big data Multi-objective job shop problems Complex networks Discovery of key nodes
This work was supported by Key Research and Development Plan Project of Shandong Province, China (No. 2017GGX201001).
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