Abstract: Because the discrete manufacturing workshop is multi-objective and multi-constraint, an energy efficiency optimization model whose optimization objective was to minimize the total energy consumption was built for discrete manufacturing workshop. Besides, an improved teaching-learningbased (TLBO) optimization algorithm for discrete energy efficiency workshop optimization was proposed. This improved algorithm introduced adaptive parameter in training phase to improve the learning efficiency and adaptability of the algorithm. In addition, second discrete process was introduced in the teaching stage and learning stage, respectively. This algorithm could be applied to the optimization of discrete manufacturing workshop under the precondition of ensuring the convergence of the algorithm is fast and strong searching ability of the characteristics. The experimental result was compared with basic teaching-learning-based optimization algorithm (TLBO), particle swarm optimization algorithm (PSO), chicken swarm optimization (CSO). Based on the analysis, optimization of this improved algorithm is superior to the other two algorithms, which indicates that the proposed algorithm is effective.

Key words: discrete manufacturing workshop, energy efficiency optimization, teaching-learning-based optimization algorithm, adaptive parameter, second discrete process