.png){kind=icon}
返回
A dynamical teaching-learning-based optimization algorithm for fuzzy energy-efficient parallel batch processing machines scheduling in fabric dyeing process
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2024.112413.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Fabric dyeing is the most time-consuming and energy-intensive process in textile production with some batch processing machines (BPMs) and uncertainty. In this study, a fuzzy energy-efficient parallel BPMs scheduling problem (FEPBSP) with machine eligibility and sequence-dependent setup time (SDST) in fabric dyeing process is investigated, and a dynamical teaching-learning-based optimization algorithm (DTLBO) is proposed to simultaneously optimize the total agreement index, fuzzy makespan, and total fuzzy energy consumption. In DTLBO, multiple classes are constructed by non-dominated sorting. Dynamical class evolution is designed, which incorporates diversified search among students and adaptive self-learning of teachers. The former is implemented using various combinations of the teacher phase and the learner phase, and the latter is achieved through teacher quality and an adaptive threshold. Additionally, a reinforcement local search based on neighborhood structure dynamic selection is also applied. Extensive experiments are conducted, and the computational results demonstrated that the new strategies of DTLBO are effective, and it is highly competitive in solving the considered problem.
Keyword:
Fabric dyeing process
Fuzzy energy-efficient scheduling
Batch processing machine
Teaching-learning-based optimization
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
机构
引用论文
.png){kind=icon}
Multiobjective evolutionary algorithms: A comparative case study and the Strength Pareto approach多目标进化算法: 比较案例研究和强度帕累托方法
PREAI
分享收藏
A bi-objective evolutionary algorithm scheduled on uniform parallel batch processing machines一种在均匀并行批处理机上调度的双目标进化算法
PREAI
分享收藏
查看更多内容