*Department of Mechanical Engineering, AAA College of Engineering & Technology, Sivakasi, India
**Department of Mechanical Engineering, Mepco Schlenk Engineering College, Sivakasi, India
Hybrid Genetic Algorithm-Simulated Annealing algorithm (HGASA) is proposed for explaining the permutation flow shop scheduling issue (PFSP) with minimizing makespan foundation. We have displayed a hybrid algorithm (HGASA) consolidates the great component of both the Genetic Algorithm and the simulated annealing algorithm. The HGASA is tried with outsized flow shop scheduling bench mark problems from OR Library and the test results are compared with earlier reported results of particle swarm optimization (PSO) algorithm, and a well-known bacterial foraging optimization algorithm (BFO). Almost, 45 famous benchmark problems were utilized to check the execution of proposed HGASA. The test comes about demonstrate that HGASA performs well with the other algorithm for all cases from the literature. The complexity of the proposed HGASA is found to be better than that of PSO and BFO. The obtained results demonstrates the viability of proposed HGASA.
Permutation Flow Shop Scheduling Problem (PFSP), Genetic Algorithm, Simulated Annealing Algorithm (HGASA), Benchmark Problem
