.png){kind=icon}
返回
Non-dominated ranked genetic algorithm for a multi-objective mixed-model assembly line sequencing problem
.png){kind=icon}
PRE
AI
DOI：10.1080/00207540903433882.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The increasing market demand for product variety forces manufacturers to design mixed-model assembly lines (MMAL) on which a variety of product models similar to product characteristics are assembled. This paper presents a method combining the new ranked based roulette wheel selection algorithm with Pareto-based population ranking algorithm, named non-dominated ranking genetic algorithm (NRGA) to a just-in-time (JIT) sequencing problem when two objectives are considered simultaneously. The two objectives are minimisation the number of setups and variation of production rates. This type of problem is NP-hard. Various operators and parameters of the proposed algorithm are reviewed to calibrate the algorithm by means of the Taguchi method. The solutions obtained via NRGA are compared against solutions obtained via total enumeration (TE) scheme in small problems and also against four other search heuristics in small, medium and large problems. Experimental results show that the proposed algorithm is competitive with these other algorithms in terms of quality and diversity of solutions.
Keyword:
mixed-model assembly line sequencing
just-in-time
Pareto-optimal frontier
non-dominated ranked genetic algorithm
Taguchi method
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.3
论文数:
1.1W
被引数:
3.7W
机构
引用论文
.png){kind=icon}
Laser-Induced Breakdown Spectroscopy (LIBS) Analysis of Calcium Ions Dissolved in Water Using Filter Paper Substrates: An Ideal Internal Standard for Precision Improvement使用滤纸基底对溶解在水中的钙离子进行激光诱导击穿光谱 (LIBS) 分析: 提高精度的理想内标
PREAI
分享收藏Solving the flowshop scheduling problem with sequence dependent setup times using advanced metaheuristics - Discrete optimization使用高级元启发式离散优化解决具有序列相关设置时间的flowshop调度问题
PREAI
分享收藏
分享收藏
查看更多内容