Proceedings of the 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)

Flexible Job Shop Scheduling Problem Based on Multi-Objective Optimization Algorithm

Authors
Li Zhang, Lu Wang
Corresponding Author
Li Zhang
Available Online March 2018.
DOI
https://doi.org/10.2991/mecae-18.2018.105How to use a DOI?
Keywords
Flexible job shop scheduling, multi-objective optimization, genetic algorithm, energy consumption
Abstract
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.

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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  -