Open AccessFlexible Job Shop Scheduling using Hybrid Swarm Intelligence
Author(s) -
S. Kavitha,
P. Venkumar
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.a1053.1291s419
Subject(s) - job shop scheduling , swarm behaviour , workload , particle swarm optimization , computer science , swarm intelligence , scheduling (production processes) , upgrade , flow shop scheduling , job shop , mathematical optimization , flexibility (engineering) , distributed computing , process (computing) , industrial engineering , artificial intelligence , engineering , algorithm , embedded system , mathematics , operating system , statistics , routing (electronic design automation)
In the present environment investigation, one of the essential tasks to be solved is scheduling. The most significant issue in the Job Shop scheduling process is the flexibility which is occurred during the manufacturing process. This paper presents the hybridization of swarm intelligence’s that is Chicken Swarm Optimization (CSO) and Discrete Fish Swarm Optimization (DFSO) to minimize the makespan, overall workload and utmost workload of the machine. The purpose of individual operators is employed to upgrade the fish position and provoke new fishes that are processing times. The purpose of this technique is to speed up the minimum convergence and trapped in the local optimum. The proposed hybrid algorithm results are compared with conventional and existing optimization approaches for a multi-objective flexible JSP process.