Dynamic Scheduling of Assembly Line Using Neural Networks for SARIA Industrial Complex-Sudan
DOI:
https://doi.org/10.54388/jkues.v2i1.187Keywords:
dynamic scheduling, modeling, assembly line, neural networksAbstract
In this paper, neural network was developed to improve the dynamic scheduling of SARIA industry complex. This work implemented into two modules: modeling system by time calculation, the main purpose of this modeling is to calculate the total manufacturing times of the products. The second module is the neural network model architecture, constructed to hold a real-time optimization schedule to solve dynamic scheduling problems. Analytical model was built, included collection and manipulation of data, time calculations and the neural networks model was formulated. Several training tests were carried out, then the dynamic scheduling formulated. To assess the validity of the system and to investigate the efficiency and robustness of the system, the results were compared with those obtained from SARIA. The results reveal that the total time of products demand is easy calculated, and the system agile to scheduling any change occur in the demand, also the proposed system reduces 4 shift days for one demand. So the developed neural network leads to minimize the total costs.
References
X. Jin, H. H. Liu, R. Gandhi, S. Kandula, R. Mahajan, M. Zhang, J. Rexford, and R. Wattenhofer, “Dynamic scheduling of network updates,” ACM SIGCOMM Computer Communication Review, vol. 44, no. 4, pp. 539–550, 2014.
C. Chen, B. Xia, B.-h. Zhou, and L. Xi, “A reinforcement learning based approach for a multiple-load carrier scheduling problem,” Journal of Intelligent Manufacturing, vol. 26, no. 6, pp. 1233–1245, 2015.
K. Kalinowski, D. Krenczyk, and C. Grabowik, “Predictive-reactive strategy for real time scheduling of manufacturing systems,” in Applied
Mechanics and Materials, vol. 307, pp. 470–473, Trans Tech Publ, 2013.
Z. Zang, W. Wang, Y. Song, L. Lu, W. Li, Y. Wang, and Y. Zhao, “Hybrid deep neural network scheduler for job-shop problem based on convolution two-dimensional transformation,” Computational Intelligence and Neuroscience, vol. 2019, 2019.
T. K. Jana, B. Bairagi, S. Paul, B. Sarkar, and J. Saha, “Dynamic schedule execution in an agent based holonic manufacturing system,”
Journal of Manufacturing Systems, vol. 32, no. 4, pp. 801–816, 2013.
X.-P. Ji, X.-B. Cao, W.-B. Du, and K. Tang, “An evolutionary approach for dynamic single-runway arrival sequencing and scheduling problem,”
Soft Computing, vol. 21, no. 23, pp. 7021–7037, 2017.
K. M. M. A. Bukkur, M. Shukri, and O. M. Elmardi, “A review for dynamic scheduling in manufacturing,” Global Journal of Research In
Engineering, 2018.
Z. Zang, W. Wang, Y. Song, L. Lu, W. Li, Y. Wang, and Y. Zhao, “Hybrid deep neural network scheduler for job-shop problem based on convolution two-dimensional transformation,” Computational Intelligence and Neuroscience, vol. 2019, 2019.
S. Vijay Anand and R. Shantha Selvakumari, “Noninvasive method of epileptic detection using dwt and generalized regression neural network,” Soft Computing, vol. 23, no. 8, pp. 2645–2653, 2019.
Z. M. Elhassan, A. I. Ahmed, and O. H. Abdalla, “Approach control of dfig rotor side converter based on grid frequency coordinated control,”
Journal of Karary University for Engineering and Science, 2021.
A. S. Santos, M. L. R. Varela, G. D. Putnik, and A. M. Madureira, “Alternative approaches analysis for scheduling in an extended manufacturing environment,” in 2014 Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC 2014), pp. 97–102, IEEE, 2014.
D. Alemo, “Smart manufacturing scheduling approaches systematic review and futuredirections.appl.sci.11,2 6.”
L. Chao Lu, X. Li, and S. Xiao, “A hybrid multi-objective grey wolf optimizer for dynamic scheduling in a real-world welding industry,”
Engineering Applications of Artificial Intelligence, vol. 57, p. 6179.
B. C¸ alis¸ and S. Bulkan, “A research survey: review of ai solution strategies of job shop scheduling problem,” Journal of Intelligent Manufacturing, vol. 26, no. 5, pp. 961–973, 2015.
B. Tripathy, S. Dash, and S. K. Padhy, “Dynamic task scheduling using a directed neural network,” Journal of Parallel and Distributed
Computing, vol. 75, pp. 101–106, 2015.
V. Kumar, S. O.J., K. G., and R., “A review on artificial neural network approach in manufacturingsystems. ”https://www.researchgate.net/publication/273695480.”
M. A. T. B. Ibrahim, M. O. S. Omer, M. M. A. Mohamed, et al., “Design and implementation of cnc writing machine based on arduino microcontroller,” Journal of Karary University for Engineering and Science, 2021
