A VND Enhanced NSGA-III Algorithm for Solving Multi-Objective Robotic Cell Scheduling Problem

2021 ◽  
Author(s):  
Xiaohui Li ◽  
Duo Gao ◽  
Yuandong Liu ◽  
Yi Zhao ◽  
Yuan Dong
Keyword(s):  
Scheduling Problem ◽  
Robotic Cell ◽  
Multi Objective ◽  
Cell Scheduling

scholarly journals A multi-objective flexible job-shop cell scheduling problem with sequence-dependent family setup times and intercellular transportation by improved NSGA-II

2021 ◽  
pp. 095440542110446
Author(s):  
Yaoyao Han ◽  
Xiaohui Chen ◽  
Minmin Xu ◽  
Youjun An ◽  
Fengshou Gu ◽  
...  
Keyword(s):  
Job Shop ◽  
Heuristic Algorithms ◽  
Setup Times ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Nsga Ii ◽  
Multi Objective ◽  
Sequence Dependent ◽  
Cell Scheduling ◽  
Dependent Setup Times

With the development of Industry 4.0 and requirement of smart factory, cellular manufacturing system (CMS) has been widely concerned in recent years, which may leads to reducing production cost and wip inventory due to its flexibility production with groups. Intercellular transportation consumption, sequence-dependent setup times, and batch issue in CMS are taken into consideration simultaneously in this paper. Afterwards, a multi-objective flexible job-shop cell scheduling problem (FJSCP) optimization model is established to minimize makespan, total energy consumption, and total costs. Additionally, an improved non-dominated sorting genetic algorithm is adopted to solve the problem. Meanwhile, for improving local search ability, hybrid variable neighborhood (HVNS) is adopted in selection, crossover, and mutation operations to further improve algorithm performance. Finally, the validity of proposed algorithm is demonstrated by datasets of benchmark scheduling instances from literature. The statistical result illustrates that improved method has a better or an equivalent performance when compared with some heuristic algorithms with similar types of instances. Besides, it is also compared with one type scalarization method, the proposed algorithm exhibits better performance based on hypervolume analysis under different instances.

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