Flexible Job Shop Scheduling Problem Based on Multi-Objective Optimization Algorithm
Li Zhang, Lu Wang
Available Online March 2018.
- https://doi.org/10.2991/mecae-18.2018.105How to use a DOI?
- Flexible job shop scheduling, multi-objective optimization, genetic algorithm, energy consumption
- Procedures of the flexible job shop scheduling problems with a workpiece can be machined with different processing time, and not necessarily equal, taking into account the green low carbon emissions is an urgent need to solve manufacturing problems, construct a to minimize the maximum completion time and the maximum energy consumption minimization model, an improved non-dominated sorting evolution algorithm is proposed (Modified non-dominated sorting genetic algorithms, MNSGA). The algorithm is mainly improved crossover and mutation in genetic manipulation to improve the global search ability of the algorithm and to prevent the emergence of local optimal solution. Finally, a set of test data is used to compare the NSGA- II algorithm and the improved multi-objective optimization algorithm MNSGA. The results show that the improved Pareto algorithm has better performance than the NSGA- algorithm in the diversity and convergence of the solution.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Li Zhang AU - Lu Wang PY - 2018/03 DA - 2018/03 TI - Flexible Job Shop Scheduling Problem Based on Multi-Objective Optimization Algorithm BT - Proceedings of the 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018) PB - Atlantis Press SP - 435 EP - 443 SN - 2352-5401 UR - https://doi.org/10.2991/mecae-18.2018.105 DO - https://doi.org/10.2991/mecae-18.2018.105 ID - Zhang2018/03 ER -