Open AccessA Hybrid Algorithm using Grey Wolf and Chicken Swarm for Flexible Job Shop Scheduling
Author(s) -
S Name,
P. Venkumar,
S. Kavitha
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.d1055.1284s219
Subject(s) - tardiness , job shop scheduling , swarm behaviour , computer science , mathematical optimization , algorithm , bat algorithm , scheduling (production processes) , robustness (evolution) , genetic algorithm , minification , metaheuristic , schedule , particle swarm optimization , mathematics , artificial intelligence , biochemistry , chemistry , gene , operating system
The Job Shop Scheduling Problem (JSSP) is one of the major inconvenience models because it is the hardest combinatorial change in nature. So far number of algorithms developed to solve JSSP and still there is scope to develop an efficient algorithm. Hence, this paper considered a unique hybrid algorithm which is the combination of the Grey Wolf and Chicken Swarm Optimization algorithm with the objective of minimization of makespan time and cost. The most extraordinary compilation (makespan), and the tardiness is considered in the meantime. The hybrid algorithm count relies upon the reenactment of the swarming behavior of wolves’ individuals. The base objectives of each wolf and swarms from most bewildering thicknesses of the group are considered as the objective work for the improvement. The results show that the proposed hybrid Grey Wolf and Chicken Swarm Optimization algorithm can obtain better results in terms of various iteration levels. The accuracy and robustness are also applied in real industrial conditions and for large size problems. The proposed method results show that very less time duration when compared with the Genetic Algorithm (GA) and Ants Colony Optimization (ACO) algorithm.