第II类双边拆卸线平衡问题建模与优化

报废汽车体积大,废弃物污染大,拆卸过程复杂,将作业任务均衡分配难。为此,本文对工作站数量确定的双边拆卸线平衡问题进行研究,建立问题数学模型,设计一种变邻域蛙跳算法。所提算法在寻优过程中采用变邻域搜索提高族群局部搜索效率;引入个体学习机制加快精英个体进化速度;提出基于二分法的节拍时间调整策略加快对最优节拍的搜索。最后,通过算例对算法性能进行验证并通过实例说明任务在拆卸线上平衡分配的重要性。

Modeling and Optimizing the Two-sided Disassembly Line Balancing Problem of Type II

Due to the large volume, various types and large waste pollution, the disassembly process of scrapped vehicles is complex. It is easy to cause the unbalanced assignment of tasks on the disassembly line, and this will affect the disassembly efficiency, as well as the labor, equipment and environmental costs. Therefore, the disassembly line for scrapped vehicles should be designed and balanced so that it can work as efficiently as possible. In this paper, we consider a two-sided disassembly line balancing problem with fixed number of workstations. Then an improved shuffled frog leaping algorithm is proposed. In the proposed algorithm, the VNS is used to improve local search efficiency. Self-learning operators are introduced to enhance the individual adaptability and jump out of the local optimum rapidly. A bound strategy based on binary search is designed to quickly adjust the cycle time. Finally, the performance of the proposed algorithm is tested by instances, and an example shows the importance of balancing the tasks among the disassembly line.