CFP last date
22 April 2024
Reseach Article

An Optimization Algorithm for Optimal Problem of Permutation Flow Shop Scheduling

by Abdel Nasser H. Zaied, Mahmoud M. Ismail, Shimaa S. Mohamed
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 173 - Number 2
Year of Publication: 2017
Authors: Abdel Nasser H. Zaied, Mahmoud M. Ismail, Shimaa S. Mohamed
10.5120/ijca2017915256

Abdel Nasser H. Zaied, Mahmoud M. Ismail, Shimaa S. Mohamed . An Optimization Algorithm for Optimal Problem of Permutation Flow Shop Scheduling. International Journal of Computer Applications. 173, 2 ( Sep 2017), 26-34. DOI=10.5120/ijca2017915256

@article{ 10.5120/ijca2017915256,
author = { Abdel Nasser H. Zaied, Mahmoud M. Ismail, Shimaa S. Mohamed },
title = { An Optimization Algorithm for Optimal Problem of Permutation Flow Shop Scheduling },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2017 },
volume = { 173 },
number = { 2 },
month = { Sep },
year = { 2017 },
issn = { 0975-8887 },
pages = { 26-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume173/number2/28308-2017915256/ },
doi = { 10.5120/ijca2017915256 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:20:11.319768+05:30
%A Abdel Nasser H. Zaied
%A Mahmoud M. Ismail
%A Shimaa S. Mohamed
%T An Optimization Algorithm for Optimal Problem of Permutation Flow Shop Scheduling
%J International Journal of Computer Applications
%@ 0975-8887
%V 173
%N 2
%P 26-34
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Nowadays the permutation flow shop scheduling problems become one of the most important problems in scheduling field. In this paper whale optimization algorithm was modified for solving PFSP. WOA is new meta-heuristic was proposed by Sayedali and Andrew in 2016 that was inspired from the nature of humpback whales movements in hunting prey. The modification is depending on two stages: firstly; WOA algorithm is converted to discrete algorithm to deal with PFSP; secondly; the mutation permutation strategy was used to improve the results of WOA. The modified algorithm is implemented on MATLAB workspace. The modified algorithm is tested with various benchmark datasets available for flow shop scheduling. The statistical results prove that the modified algorithm (MWOA) is competent and efficient for solving flow shop problems.

References
  1. Hong-Qing. Z, Yong-Quan. Z, and Cong. X, 2016. “Modified Cuckoo Search Algorithm for Solving Permutation Flow Shop Problem”, Springer International Publishing Switzerland, pp. 714–721.
  2. M. K. Marichelvam1 & M. Geetha, 2013. “Solving Flowshop Sceduling Problem using a Discrete African Wild DOG Algorithm”, ICTACT journal on Soft Computing, pp. 555 – 558.
  3. Deepanshu .A and Gopal .A, 2016. “Meta-heuristic approaches for flowshop scheduling problems: a review”, Advanced Operations Management, pp. 1 – 16.
  4. M. F. Tasgetiren, Yun-Chia. L, Mehmet. S and Gunes. G, 2007. ‘”A particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem”, European Journal of Operational Research, pp. 1930–1947.
  5. Hongcheng. L, Liang. G and Quanke. P, 2011. “A hybrid particle swarm optimization with estimation of distribution algorithm for solving permutation flowshop scheduling problem”, Expert Systems with Applications, pp. 4348–4360.
  6. Vanita. G. T and Pravin. K, 2013. “Solving Permutation Flowshop Scheduling Problem Using Improved Differential Evolutionary Algorithm”, International Journal of Engineering Research & Technology (IJERT), pp. 456-461.
  7. Simon. F, Hui-long. L, Yan. Z, Suash. D , Thomas. H, 2014. “Solving the Permutation Flow Shop Problem with Firefly Algorithm”, 2nd International Symposium on Computational and Business Intelligence, pp. 25-29.
  8. A. Baskar, 2015. “Minimizing the Makespan in Permutation Flow Shop Scheduling Problems using Simulation”, Indian Journal of Science and Technology, pp. 1-7.
  9. Kannan. G, R. Balasundaram, N. Baskar and P. Asokan, 2017. “A Hybrid Approach for Minimizing Makespan in Permutation Flowshop Scheduling”, Systems Engineering Society of China and Springer-Verlag Berlin Heidelberg, pp. 50-76.
  10. Seyedali. M and Andrew. L, 2016. “The Whale Optimization Algorithm”,Advances in Engineering Software, pp. 51–67.
  11. Hongping. H, Yanping. B and Ting. X, 2016. “A whale optimization algorithm with inertia weight”, WSEAS Transaction on Computers, pp. 319-326.
  12. Seyedali. M, 2016. “Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems”, Neural Comput & Applic, pp. 1053–1073.
  13. Carlier. J, 1978. “Ordonnancements a contraintes disjonctives”, R.A.I.R.O. Recherche Operationelle/Oper. Res. 12, 333–351.
  14. Reeves. C. R, 1995. “A genetic algorithm for flow-shop sequencing”, Comput. Oper. Res. 22, 5–13.
  15. Taillard, E, 1993. “Benchmarks for basic scheduling instances”, European Journal of Operational Research, 64(2): 278-285.
  16. M. Saravanan, A. N. Haq, A. R. Vivekraj and T. Prasad, 2008. “Performance evaluation of the scatter search method for permutation flowshop sequencing problems”, Int J Adv Manuf Technol, pp.1200–1208.
  17. Shih-Hsin. Ch, Pei-Chann. Ch, T. C. E. Cheng, Qingfu. Z, 2012. “A Self-guided Genetic Algorithm for permutation flowshop scheduling problems”, Computers & Operations Research, pp.1450–1457.
  18. Dipak. L and Uday. K. Ch, 2009. “An efficient hybrid heuristic for makespan minimization in permutation flow shop scheduling”,Int J Adv Manuf Technol, pp.559–569.
Index Terms

Computer Science
Information Sciences

Keywords

Permutation flow shop scheduling problem Whale Optimization Algorithm meta-heuristic algorithm MWOA.