Supply chain scheduling optimization based on genetic particle swarm optimization algorithm
- 41 Downloads
In order to optimize supply chain scheduling problem in mass customization mode, the mathematical programming of supply chain scheduling optimization problem is modelled. At the same time, model mapping is defined as a directed graph to facilitate the application of intelligent search algorithms. In addition, the features of genetic algorithm and particle swarm algorithm are introduced. Genetic algorithm has a strong global search capability, and particle swarm optimization algorithm has fast convergence speed. Therefore, the two algorithms are combined to construct a hybrid algorithm. Finally, the hybrid algorithm is used to solve the supply chain optimization scheduling problem model. Compared with other algorithms, the results show that the hybrid algorithm has better performance. The mathematical programming model used in this paper can be further extended and improved.
KeywordsScheduling optimization Hybrid algorithm Genetic algorithm Particle swarm algorithm
The authors acknowledge the National Natural Science Foundation of China (Grant: 111578109), the National Natural Science Foundation of China (Grant: 11111121005).
- 10.Maolong, X.I., Xiaojun, W.U., Fang, W., et al.: Quantum-behaved particle swarm optimization algorithm with self-renewal mechanism. Comput. Eng. Appl. 2(10), 100–114 (2015)Google Scholar
- 12.Xia, L.R., Run-Xue, L.I., Liu, Q.Y., et al.: An adaptive multi-objective particle swarm optimization algorithm based on dynamic AHP and its application. Control Decis. 30(2), 15–221 (2015)Google Scholar