<![CDATA[Editorial department of the Journal of National University of Defense Technology -->Decision and Application Based on Artificial Intelligence Optimization Algorithms]]>

<![CDATA[Editorial department of the Journal of National University of Defense Technology -->Decision and Application Based on Artificial Intelligence Optimization Algorithms]]> <![CDATA[Review of green shop scheduling problem]]> 2025/4/14 0:00:00 GAO Liang, YU Fei, LU Chao 4true <![CDATA[Robotic parallel disassembly sequence planning method based on reinforcement learning and genetic algorithm]]> Q learning was used to select the best crossover and mutation strategies in the iteration process to enhance the algorithms adaptability. Finally, in an engine disassembly case with 34 tasks, the superiority of the proposed algorithm was verified by comparing with four classic multi-objective algorithms. The analysis of the disassembly schemes shows that the robotic parallel disassembly mode can effectively shorten the completion time and reduce disassembly energy consumption.]]> 2025/4/14 0:00:00 WANG Kaipu, MA Xiaoyi, LU Chao, YIN Lüjiang, LI Xinyu 3true <![CDATA[Improved co-evolutionary algorithm for solving many-objective cloud workflow scheduling problem]]> 2025/4/14 0:00:00 ZHOU Jiajun, JI Xiaohui, LU Chao, GAO Liang 2true <![CDATA[Curriculum reinforcement learning algorithm for flexible job shop scheduling problems]]> 2025/4/14 0:00:00 LU Chao, XIAO Yang, ZHANG Biao, GAO Liang 1true