Balancing problem of multi-objective mixed-model assembly line based on IWD algorithm

被引:0
|
作者
Li M. [1 ,2 ]
Zhang Y. [1 ]
Zhou H. [1 ]
机构
[1] School of Mechanical Engineering, Xiangtan University, Xiangtan
[2] School of Mathematics and Computational Science, Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Xiangtan
关键词
Intelligent water drop algorithm; Mixed-model assembly line; Pareto dominant; Task relatedness;
D O I
10.13196/j.cims.2016.04.010
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
Based on considering the fluctuations of switching product, for solving mixed-model assembly line balancing problem, an improved Intelligent Water Drop (IWD) algorithm was proposed, which integrated three optimization objectives-workstation number, workload balance and task relatedness. The node metastasis rule of IWD algorithm was improved by adding largest probability leading rule and random search rule. The method of Pareto dominance was used to obtain frontier solution set and provide a heuristic value for each particle, and all particles were conducted global update to enhance global search ability according to the heuristic value. Through the experiment of standard test problems, the results showed that the improved IWD algorithm could solve the multi-objective mixed-model assembly line balancing problem more effectively than genetic algorithm. © 2016, Editorial Department of CIMS. All right reserved.
引用
下载
收藏
页码:965 / 973
页数:8
相关论文
共 17 条
  • [1] Zhang Z., Cheng W., Zhong B., Et al., Hybrid behavior ant colony optimization for mixed-model assembly line balancing problem, Journal of Mechanical Engineering, 45, 5, pp. 95-101, (2009)
  • [2] Yu Z., Su P., Combining genetic algorithm and simulation analysis for mixed-model assembly, Computer Integrated Manufacturing Systems, 14, 6, pp. 1120-1129, (2008)
  • [3] Hamta N., Fatemi Ghomi S.M.T., Jolai F., Et al., A hybrid PSO algorithm for a multi-objective assembly line balancing problem with flexible operation times, sequence-dependent setup times and learning effect, International Journal of Production Economics, 141, 1, pp. 99-111, (2013)
  • [4] Yang C., Gao J., Sun L., Multi-objective mix-model assembly line rebalancing:model and algorithm, Systems Engineering-Theory & Practice, 33, 8, pp. 1956-1964, (2013)
  • [5] Zhang W., Gen M., An efficient multi-objective genetic algorithm for mixed-model assembly line balancing problem considering demand ratio-based cycle time, Journal of Intelligent Manufacturing, 22, 3, pp. 367-378, (2011)
  • [6] Manavizadeh N., Hosseini N., Rabbani M., Et al., A simulated annealing algorithm for a mixed model assembly u-line balancing type-I problem considering human efficiency and just-in-time approach, Computers & Industrial Engineering, 64, 2, pp. 669-685, (2013)
  • [7] Chutima P., Chimklai P., Multi-objective two-side mixed-model assembly line balancing using particle swarm optimization with negative knowledge, Computers & Industrial Engineering, 62, 1, pp. 39-55, (2012)
  • [8] Dou J., Su C., Li J., Discrete particle swarm optimization algorithms for assembly line balancing problems of type I, Computer Integrated Manufacturing Systems, 18, 5, pp. 1021-1030, (2012)
  • [9] Ponnambalam S.G., Aravindan P., Naidu G.M., A multi-objective genetic algorithm for solving assembly line balancing problem, The International Journal of Advanced Manufacturing Technology, 16, 5, pp. 341-352, (2000)
  • [10] Shah-Hosseini H., Problem solving by intelligent water drops, Proceedings of IEEE Congress on Evolutionary Computation, pp. 3226-3231, (2007)