Dynamic Scheduling of Material Delivery Based on Neural Network and Knowledge Base

ZHOU Binghai, ZHU Zhexin

Abstract

  In order to tackle the dynamic scheduling problem of tow trains in mixed-model assembly lines, a scheduling approach is proposed based on the knowledge base and neural network. Firstly, the dynamic scheduling problem of material delivery in the automotive assembly line is formally described. The throughput of the assembly line and the total delivery distances are selected as components of the objective function. After that，the sample data of mixed-model assembly lines are generated by the Plant Simulation software and are used to train the neural network model offline. Finally, the trained neural network model and the knowledge base are adopted in the real-time scheduling process to select the optimal scheduling rule for tow trains. The experimental results indicate that the scheduling rules selected by the selection method proposed in the paper are mostly the optimal ones. The lower computational complexity of scheduling rules ensures the real-time performance of scheduling. It can cope well with changes in the dynamic environment, thus effectively improving the dynamic scheduling of tow trains.

 

Keywords: mixed-model assembly line,  dynamic scheduling,  material handling,  neural network,  artificial intelligence


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References


CHANG Q，PAN C Y，XIAO G X，et al. Integrated modeling of automotive assembly line with material handling [J]. Journal of Manufacturing Science and Engineering，2013，135(1)：011018.

ZHOU B H，XU J H. SVM-based real-time scheduling approach of multi-load carriers[J]. Journal of Jilin University(Engineering and Technology Edition)，2016，46(6)：2027—2033.(In Chinese)

EMDE S，ABEDINNIA H，GLOCK C H. Scheduling electric vehicles making milk-runs for just-in-time delivery [J]. IISE Transactions，2018，50(11)：1013—1025.

FATHI M，ALVAREZA M J，RODRIGUEZB V，et al. A multiobjective optimization algorithm to solve the part feeding problem in mixed-model assembly lines [J]. Mathematical Problems in Engineering，2014，11(1)：809—812.

FATHI M，RODRIGUEZ V，ALVAREZ M J. A novel memetic ant colony optimization-based heuristic algorithm for solving the assembly line part feeding problem[J]. International Journal of Advanced Manufacturing Technology，2014，75(1)：629—643.

FATHI M，RODRIGUEZ V，FONTES D B M M，et al. A modified particle swarm optimisation algorithm to solve the part feeding problem at assembly lines [J]. International Journal of Production Research，2015，54(3)：878—893.

SHIUE Y R，LEE K C，SU C T. Real -time scheduling for a smart factory using a reinforcement learning approach [J]. Computers & Industrial Engineering，2018，125：604—614.

SEKER A，EROL S，BOTSALI R. A neuro -fuzzy model for a new hybrid integrated process planning and scheduling system [J]. Expert Systems with Applications，2013，40(13)：5341—5345.

FAGAN D，FENTON M，LYNCH D，et al. Deep learning through evolution：a hybrid approach to scheduling in a dynamic environmen[t C]// International Joint Conference on Neural Networks. Anchorage，Alaska：Institute of Electrical and Electronics Engineers， 2017：775—782.

ZANG Z L，WANG W L，SONG Y H，et al. Hybrid deep neural network scheduler for job-shop problem based on convolution two-dimensional transformation[J]. Computational Intelligence and Neuroscience，2019，2019(2)：1—19.

SONG Y，SHI G，CHEN L Y，et al. Remaining useful life prediction of turbofan engine using hybrid model based on autoencoder and bidirectional long short -term memory [J]. Journal of Shanghai Jiaotong University(Science)，2018，23(S1)：85—94.

ZHUO P C，ZHU Y，WU W X，et al. Real-time fault diagnosis for gas turbine blade based on output -hidden feedback elman neural network [J]. Journal of Shanghai Jiaotong University(Science)， 2018，23(S1)：95—102.

WANG L，SHI Y L，LIU S. An improved fruit fly optimization algorithm and its application to joint replenishment problems [J]. Expert Systems with Applications，2015，42(9)：4310—4323.

SPECHT D F. A general regression neural network [J]. IEEE Transactions on Neural Networks，1991，2(6)：568—576.

CHEN C，XI L F，ZHOU B H，et al. A multiple -criteria real -time scheduling approach for multiple -load carriers subject to LIFO loading constraints [J]. International Journal of Production Research，2011，49(16)：4787—4806.


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