.png){kind=icon}
返回
A bi-objective evolutionary algorithm scheduled on uniform parallel batch processing machines
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2022.117487.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper addresses the problem of minimizing the maximum lateness and the total pollution emission costs by scheduling a group of jobs with different processing times, sizes, release times, and due dates on uniform parallel batch processing machines with non-identical machine capacities and different unit pollution emission costs. We develop a discrete bi-objective evolutionary algorithm C-NSGA-A to solve this problem. On the one hand, we present a method of constructively generating an individual with the first job selection to produce an initial population for improving the convergence of individuals. On the other hand, we propose an angle-based environmental selection strategy to choose individuals to maintain the diversity of individuals. Through extensive simulation experiments, C-NSGA-A is compared with several state-of-the-art algorithms, and experimental results show that the proposed algorithm performs better than those algorithms. Moreover, the proposed algorithm has more obvious advantages on instances with a larger number of jobs.
Keyword:
Scheduling
Uniform parallel batch processing machines
Lateness
Total cost
Evolutionary algorithm
期刊
IF:
7.5
论文数:
2.9W
被引数:
10.2W
机构
引用论文
Surveillance for human immunodeficiency virus type 1 group O infections in the United States.png)
Transfusion
TransfusionIF0
PREAI
分享收藏A hybrid genetic heuristic for scheduling parallel batch processing. machines with arbitrary job sizes用于调度并行批处理的混合遗传启发式方法。具有任意作业大小的机器
PREAI
分享收藏分享收藏
查看更多内容