Abstract: Aiming at the shortage of constraints on the relationship between tasks in the existing research on the disassembly problem of human-robot share workstation,the human-robot share workstation parallel disassembly line balancing problem was put forward,and its multi-objective mixed integer programming model was constructed with the optimization objectives of the number of workstations,the idle time balance index,the total number of operators and the disassembly cost.A hybrid teaching-learning based optimization algorithm was proposed,which integrated self-study operation and simulated annealing operation on the basis of the original Teaching-Learning Based Optimization (TLBO) algorithm to keep the diversity of population and avoid the algorithm falling into local optimum.The mathematical programming software LINGO was used to accurately solve the small and medium-sized examples of the human-robot share workstation parallel disassembly line balancing problem,and the results were compared with those of the hybrid TLBO algorithm to verify the correctness of the model and the effectiveness of the algorithm.The model and the algorithm were applied to solve the large-scale example of human-robot share workstation parallel disassembly line balancing problem.The superiority of the algorithm in solving performance and quality was further verified through different comparative experiments with various algorithms.

Key words: human-robot share workstation, parallel disassembly line, teaching-learning based optimization, mixed integer programming model, multi-objective optimization

摘要： 针对现有人机共站拆卸问题研究对任务间影响关系约束的不足,提出人机共站并行拆卸线平衡问题,以工作站开启数、空闲时间均衡指标、操作者总数和拆卸成本为优化目标,构建了其多目标混合整数规划模型。并提出一种混合教学优化算法求解,算法在原始教学优化算法的基础上融入自学操作和模拟退火操作,以保持种群多样性并避免算法陷入局部最优。应用数学规划软件LINGO精确求解所提问题的中小规模算例并与算法求解结果对比,验证了所建模型正确性与算法的有效性。最后,将所建模型与所提算法应用至大规模人机共站并行拆卸线实例中,通过与多种算法进行不同的对比试验,进一步验证了所提算法求解性能和求解质量的优越性。

关键词: 人机共站, 并行拆卸线, 教学优化算法, 混合整数规划模型, 多目标优化