MULTI-OBJECTIVE OPTIMIZATION USING HYBRID ALGORITHM AND ITS APPLICATION TO SCHEDULING IN FLOW SHOPS

In this paper, a hybrid algorithm has been developed to solve the permutation flow shop scheduling problem. For the effective working of the proposed hybrid algorithm, genetic algorithm is combined with the simulated annealing algorithm. In the genetic algorithm, the initial population is created by utilizing the prominent Nawaz, Enscore & Ham (NEH) heuristic. Here, the problem is optimized by considering three criteria, makespan, total flow time and machine idle time and the equivalent weights of makespan, total flow time and machine idle time are considered. Since these issues are not known firmly NP-hard, Hybrid Genetic Algorithm Simulated Annealing (HGASA) based meta-heuristic approach is proposed. The execution of the proposed HGASA algorithm is shown by applying it to the standard benchmark problem accessible in the OR-Library. Calculation results in view of some change flow shop scheduling benchmark problem demonstrate that the HGASA gives better solution compared to Multi Objective Improved Genetic Algorithm (MOIGA).