Flexible Job Shop Scheduling using Hybrid Swarm Intelligence
S. Kavitha1, P. Ven Kumar2
1S. Kavitha, Department of Mechanical Engineerig, Kalasalingam Academy of Research and Education College, Krsihnankovil (Tamil Nadu), India.
2P.Ven Kumar, Department of Mechanical Engineerig, Kalasalingam Academy of Research and Education College, Krsihnankovil (Tamil Nadu), India.
Manuscript received on 25 November 2019 | Revised Manuscript received on 19 December 2019 | Manuscript Published on 30 December 2019 | PP: 1069-1075 | Volume-9 Issue-1S4 December 2019 | Retrieval Number: A10531291S419/19©BEIESP | DOI: 10.35940/ijeat.A1053.1291S419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Flexible Job Shop Scheduling, Hybrid Algorithm, Multi Objectives, Swarm Intelligence and Processing Times.
Scope of the Article: Swarm Intelligence