A FLOW SHOP SCHEDULING ALGORITHM BASED ON  ARTIFICIAL NEURAL NETWORK

Harendra Kumar; Shailendra Giri

doi:10.48165/

Authors

Harendra Kumar Department of Mathematics and Statistics, Gurukula Kangri Vishwavidyalaya, Haridwar, Uttarakhand 249404, India
Shailendra Giri Department of Mathematics and Statistics, Gurukula Kangri Vishwavidyalaya, Haridwar, Uttarakhand 249404, India

DOI:

https://doi.org/10.48165/

Keywords:

Flow shop scheduling, artificial neural networks, sequence

Abstract

In current years, artificial neural networks (ANNs) are of interest to researchers in many areas for different reasons. It has proved to be a good tool to solve many varieties of problems. This paper suggests an idea for the n jobs and m machines flow shop scheduling problems by applying the artificial neural network technique. The major purpose of this paper is to get the job sequence that minimizes the makespan. The performance of our suggested neural network system is compared with the immediate methods which are taken from different papers. A comparison of the procedure developed by us here with the other available methods available in the literature is also provided in this paper.

References

. Ahmad, Q. S. and Khan, M. H. (2012). Application of neural network to scheduling problem including transportation time, International Journal of Computer Application, Vol.54, No.5, 8-10. [2]. Akyol, D. E. (2004). Application of neural networks to heuristic scheduling algorithms, Computers & Industrial Engineering, Vol.46, No.4, 679-696.

. Arizono, I., Yamamoto, A. and Ohta, H. (1992). Scheduling for minimization total actual flow time by neural networks, International Journal of Production Research, Vol.30, No.3, 503-511. [4]. Baskar, A. (2015). Minimizing the makespan in permutation flow shop scheduling problem using simulation, Indian Journal of Science and Technology, Vol.8, No.22, 1-7.

. Baskar, A. and Anthony, X. M. (2012). A new heuristic algorithm using Pascal’s Triangle to determine more than one sequence having optimal / near optimal makespan in flow shop scheduling problems, International Journal of Computer Applications, Vol.39, No.5, 9-15.

. Centobelli, P., Converso, G., Murino, T. and Santillo, L. C. (2016). Flow shop scheduling algorithm to optimize warehouse activities, International Journal of Industrial Engineering Computations, Vol.7, No.1, 49-66.

. Chaudhry, I., A. and Khan, A., M. (2012). Minimizing makespan for a no wait flowshop using genetic algorithm, Sadhana, Indian Academy of Sciences, Vol. 37, No. 6, 695-707.

. El-Bouri, A., Balakrishnan, S. and Popplewell, N. (2000). Sequencing jobs on a single machine: A neural network approach, European Journal of Operational Research, Vol.126, No.3, 474-490. [9]. Feng, S., Ling, L., Ling, C. and Huang, J. (2003). Using MLP networks to design a production scheduling system, Computer & Operations Research Vol.30, No.6, 821-832.

. Garey, M. R., Johnson, D. S. and Sethi, R. (1976). The complexity of flowshop and jobshop scheduling, Mathematics of Operation Research, Vol.1, No.2, 117-129.

. Gonzalez-Neira, E. M., Montoya-Torres, J. R. and Barrera, D. (2017). Flow shop scheduling problem under uncertainties: Review and trends, International Journal of Industrial Engineering Computations, Vol. 8, No. 4, 339-426.

. Haq, A. N. and Ramanan, T. R. (2006). A bicriterion flow shop scheduling using artificial neural network, Int. J. Adv. Manuf. Technol, Vol.30, No.11-12, 1132-1138.

. Ignall, E. and Schrage, L. (1965). Application of the branch and bound technique to some flowshop scheduling problems, Operation Research, Vol.13, No.3, 400-412.

. Jain, A. S. and Meeran, S. (1998). Job shop scheduling using neural networks, International Journal of Production Research, Vol.36, No.5, 1249-1272.

. Johnny, C. H. and Yih-Long (1991). A new heuristic for the n jobs, m machines flow shop problem, European Journal of Operational Research, Vol.52, No.2, 194-202.

. Johnson, S. M. (1954). Optimal two and three stage production schedules with setup times included, Naval Research Logistics Quarterly, Vol.1, No.1, 61-68.

. Kangarloo, N., Rezaeian, J. and Khosrawi, X. (2016). JIT Scheduling problem on flexible flow shop with machine breakdown, machine eligibility and setup times, Journal of Mathematics and Computers Science, Vol.16, No.1, 50-58.

. Koulamas, C. (1998). A new constructive heuristic for the flow shop scheduling problem, European Journal of Operational Research, Vol.105, No.1, 66-71.

. Krishna, V. G. and Vijay, P. (2013). A proposed method for permutation flow shop scheduling, International Journal of Emerging Technology and Advanced Engineering, Vol.3, No.4, 209-213. [20]. Kumar, G. and Singhal, S. (2013). Genetic algorithm optimization of flow shop scheduling problem with sequence dependent setup time and lot splitting, International Journal of Engineering Business and Enterprise Applications ( IJEBEA), Vol.4, No.1, 62-71.

. Kumar, H., Kumar, P. and Sharma, M. (2018). An effective new hybrid optimization algorithm for solving flow shop scheduling problems, Malaya Journal of Matematik, Vol. 5, No. 1, 107-113. [22]. Lebbar, G., Barkany, A. E., Jabri, A. and Abbassi, I. E. (2018). Hybrid metaheuristics for solving the

blocking flowshop scheduling problem, International Journal of Engineering Research in Africa, Vol. 36, No. 1, 124-136.

. Lee, I. and Shaw, M. J. (2000). A neural-net approach to real time flow-shop sequencing, Computer & Industrial Engineering, Vol.38, No.1, 125-147.

. Modrak, V. and Pandian, R. S. (2010). Flow shop scheduling algorithm to minimize completion time for n-jobs m-machine problem, Technical Gazette, Vol.17, No.3, 273-278.

. Palmer, D. (1965). Sequencing jobs through a multi stage processing the minimum total time a quick method of obtaining a near optimum, Operation Research, Vol.16, No.1, 45-61.

. Ramanan, T. R., Sridharan, R., Shashikant, K. S. and Haq, A. N. (2011). An artificial neural network based heuristic for flow shop scheduling problems, J. Intell. Manuf., Vol.22, 279-288. [27]. Rouhani, S., Fathian, M., Jafari, M. and Akhavan, P. (2010). Solving the problem of flow shop scheduling by neural network approach, Springer-Verlag Berlin Heidelberg, CCIS 88, 172-183. [28]. Sabuncuoglu, I. and Gurgun, B. (1996). A neural network model for scheduling problems, European Journal of Operational Research, Vol.93, No.2, 288-299.

. Sache, M. R. G. (2014). Neural network for solving job shop scheduling problem, IOSR Journal of Computer Engineering, Vol.16, No.6, 18-25.

. Singhal, E. and Hemrajani, N. (2013). An improved NEH algorithm applied to permutation flow shop scheduling, International Journal of Engineering Science and Research, Vol.2, No.5, 1164-1170. [31]. Zini, H. and El-Bernoussi, S. (2015). A discrete particle swarm optimization with combined priority dispatching rules for hybrid flow shop scheduling problem, Applied Mathematical Sciences, Vol.9, No.24, 1175-1187.

. Aslan, D. (1999). Model development and application based on object oriented neural network for scheduling problem. DEU Res. Fund. Proj., Nr. 0908.97.07.01. University of Dokuz Eylul, Izmir.