Abstract
In this paper, the flowshop scheduling problem with the objective of minimizing the makespan has important applications in an exceedingly type of industrial systems. The main concern of flow shop scheduling is to get the most effective sequence, that minimizes the makespan, time of flow, time of idle, delay, etc. the objective of minimizing makespan is planned for finding the flowshop scheduling problem with Effective Genetic algorithm (EGA). EGA could be an easy and efficient algorithm that is employed to resolve for each single and multi-objective problem in flow shop environment. This algorithm can works simply for our real life applications. The planned algorithm is tested with well-known problems in literature. EGA’s resolution performance has been compared with the present results reported by researchers. The obtained results show that the planned EGA performs higher than NPFS-ACO algorithms in finding the flowshop scheduling problem with the makespan criterion as average percentage improvement of 1.42%. This improvement ends up in two completely different meta-heuristic algorithms for finding flow shop planning problems specifically real coded Genetic algorithm (RCGA) and EGA. However EGA is performed well once comparing with RCGA. DOI: http://dx.doi.org/10.5755/j01.mech.23.4.15053
Highlights
In past 5 decades flow shop scheduling may be a challenging area for researchers
During this effective genetic algorithm (EGA), worst solutions are aloof from that algorithm by adding robust factor concept
Rajendran [2, 3] given one branch-and-bound algorithm and two heuristic algorithms aimed at two machine flow shop scheduling problem through makespan because the primary criterion and total flow time
Summary
In past 5 decades flow shop scheduling may be a challenging area for researchers. Its main aim is to work out the job sequence of processing jobs on a given set of machines. We thought of minimizing makespan as objective for my present work using meta-heuristic approach by improvising the Genetic algorithmic program. Rajendran [2, 3] given one branch-and-bound algorithm and two heuristic algorithms aimed at two machine flow shop scheduling problem through makespan because the primary criterion and total flow time. This study is that the initial application of ACO metaheuristic to multiobjective m-machine flow shop scheduling problem with esteem to the both objectives of makespan and total flow time. Computational studies were showed on the yardstick problems from Taillard [10] because the test problem so as to verify the algorithm’s performance During this literature survey we tend to found several algorithm are accustomed solve flow shop scheduling problem in manufacturing field by many of the researchers, we might found that genetic algorithm is very old algorithm very powerful algorithm to solve flowshop scheduling problem. We tend to addressed effective genetic algorithm (EGA) to solve flowshop benchmark problem, and the results obtained by EGA are compared with earlier reportable results by using ant colony algorithm ACO by Betul Yagmahan [11] and Andrea Rossi [12]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
