• Deepanshu Arora
• Gopal Agarwal

In this paper, we present GA and PSO for multi-objective function for flow-shop scheduling problem with various weighted sets. For this work we used the multi-target function which includes four objectives are total weighted squared tardy, total weighted squared earliness, maximum Makespan, no. of tardy jobs. The multi-objective function contained here is the minimization of weighted sum of all four objectives. As increases the size of the problem solving time also increases (NP-Hard Problem) so we use the Meta heuristic approach to solve the problem. Genetic Algorithm GA and Particle Swarm Optimization PSO are the most important technique of meta-heuristic approaches capable to handle Nonlinear NP-Hard large size of problems. Comparison between the results of the GA and PSO shows that in larger problems the results obtained by PSO do not have adequate efficiency compare to GA.

Flow shop scheduling problem; genetic algorithm(GA); particle swarm optimization (PSO)

"Application of GA and PSO for Flow-Shop Scheduling Problem", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.4, Issue 9, page no.240-247, September-2017, Available :http://www.jetir.org/papers/JETIR1709042.pdf

"Application of GA and PSO for Flow-Shop Scheduling Problem", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.4, Issue 9, page no. pp240-247, September-2017, Available at : http://www.jetir.org/papers/JETIR1709042.pdf