scholarly journals Sequential Scheduling Method for FJSP with Multi-Objective under Mixed Work Calendars

Processes ◽  
2019 ◽  
Vol 7 (12) ◽  
pp. 888
Author(s):  
Qiang Zeng ◽  
Menghua Wang ◽  
Ling Shen ◽  
Hongna Song
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Genetic Operators ◽  
Nsga Ii ◽  
Multi Objective ◽  
Elite Strategy ◽  
Scheduling Method ◽  
Encoding Method

A sequential scheduling method for multi-objective, flexible job-shop scheduling problem (FJSP) work calendars is proposed. Firstly, the sequential scheduling problem for the multi-objective FJSP under mixed work calendars was described. Secondly, two key technologies to solve such a problem were proposed: one was a time-reckoning technology based on the machine’s work calendar, the other was a sequential scheduling technology. Then, a non-dominated sorting genetic algorithm with an elite strategy (NSGA-II) was designed to solve the problem. In the algorithm, a two-segment encoding method was used to encode the chromosome. A two-segment crossover and mutation operator were used with an improved strategy of genetic operators therein to ensure feasibility of the chromosomes. Time-reckoning technology was used to calculate start and end time of each process. The sequential scheduling technology was used to implement sequential scheduling. The case study shows that the proposed method can obtain an effective Pareto set of the sequential scheduling problem for multi-objective FJSP under mixed work calendars within an acceptable time.

scholarly journals NSGA-II algorithm and application for multi-objective flexible workshop scheduling

2020 ◽  
Vol 14 ◽  
pp. 174830262094246
Author(s):  
Wang Yahui ◽  
Shi Ling ◽  
Zhang Cai ◽  
Fu Liuqiang ◽  
Jin Xiangjie
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Optimal Solution ◽  
Scheduling Problem ◽  
Objective Functions ◽  
Nsga Ii ◽  
Industrial Processing ◽  
Multi Objective ◽  
Processing Cost

Based on the study of multi-objective flexible workshop scheduling problem and the learning of traditional genetic algorithm, a non-dominated sorting genetic algorithm is proposed to solve and optimize the scheduling model with the objective functions of processing cycle, advance/delay penalty and processing cost. In the process of optimization, non-dominated fast ranking operator and competition operator are used to select the descendant operator, which improves the computational efficiency and optimization ability of the algorithm. Non-repetitive non-dominant solutions and frontier sets are found in the iteration operation to retain the optimal results. Finally, taking a manufacturing workshop as an example, the practicability of the proposed algorithm is verified by the simulation operation of the workshop scheduling information and the comparison with other algorithms. The results show that the algorithm can obtain the optimal solution more quickly than the unimproved algorithm. The improved algorithm is faster and more effective in searching, and has certain feasibility in solving the job shop scheduling problem, which is more suitable for industrial processing and production.

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