scholarly journals Decomposition-based multi-objective optimization for energy-aware distributed hybrid flow shop scheduling with multiprocessor tasks

2021 ◽  
Vol 26 (5) ◽  
pp. 646-663
Author(s):  
Enda Jiang ◽  
Ling Wang ◽  
Jingjing Wang
2014 ◽  
Vol 1082 ◽  
pp. 529-534
Author(s):  
Zheng Ying Lin ◽  
Wei Zhang

Due to several mutual conflicting optimized objectives in the hybrid flow shop scheduling problem, its optimized model, including three objectives of make-span, flow-time and tardiness, was firstly set up, instead of the single optimized objective. Furthermore, in order to improve the optimized efficiency and parallelism, after comparing the normal multi-objective optimized methods, an improved NSGA-II algorithm with external archive strategy was proposed. Finally, taking a piston production line as example, its performance was tested. The result showed that the multi-objective optimization of hybrid flow shop scheduling based on improved NSGA-II provided managers with a set of feasible solutions for selection in accordance to their own preference. Therefore the decision could be made more scientific and efficient, and thus brings to the factory more economic benefits.


IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 151180-151194 ◽  
Author(s):  
Qiang Cheng ◽  
Chenfei Liu ◽  
Hongyan Chu ◽  
Zhifeng Liu ◽  
Wei Zhang ◽  
...  

2019 ◽  
Vol 95 ◽  
pp. 03008
Author(s):  
Zhao Zijin ◽  
Wang aimin ◽  
Ge Yan ◽  
Lin Lin

To deal with the multi-objective hybrid flow Shop Scheduling Problem (HFSP), an improved genetic algorithms based on parallel sequential moving and variable mutation rate is proposed. Compared with the traditional GA, the algorithm proposed in this paper uses the two-point mutation rule based on VMR to find the global optimum which can make the algorithm jump out of the local optimum as far as possible, once it falls into the local optimum quickly. Decoding rules based on parallel sequential movement ensures that the artifact can start processing in time, so that the buffer between stages in the flow-shop is as little as possible, and the production cycle is shortened. Finally, a program was developed with the actual data of a workshop to verify the feasibility and effectiveness of the algorithm. The result shows that the algorithm achieves satisfactory results in all indexes mentioned above.


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