Energy Efficiency Data Mining and Scheduling Optimization of Discrete Workshop

doi:10.16182/j.issn1004731x.joss.19-FZ0257

Abstract

Abstract: This paper addresses the optimization of energy consumption in discrete workshops and establishes the energy efficiency optimization model of discrete workshops. The relationship between data mining and knowledge discovery is established. Through scheduling data preprocessing and C4.5 decision tree learning algorithm, the discovery of scheduling knowledge is realized. Energy efficiency optimization calculation is achieved in discrete workshops by the combination of scheduling knowledge and improved differential evolution algorithm (IDE). By comparing with TLBO, GA and PSO, the feasibility of IDE algorithm is verified.

Key words: energy efficiency, data mining, scheduling knowledge, differential evolution algorithm

CLC Number:

TP391.9

Lin Yugu, Wang Yan. Energy Efficiency Data Mining and Scheduling Optimization of Discrete Workshop[J]. Journal of System Simulation, 2019, 31(12): 2702-2711.

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