Hybrid genetic algorithm for bi-criteria objectives in scheduling process

Scheduling of multiobjective problems has gained the interest of the researchers. Past many decades, various classical techniques have been developed to address the multiobjective problems, but evolutionary optimizations such as genetic algorithm, particle swarm, tabu search method and many more are being successfully used. Researchers have reported that hybrid of these algorithms has increased the efficiency and effectiveness of the solution. Genetic algorithms in conjunction with Pareto optimization are used to find the best solution for bi-criteria objectives. Numbers of applications involve many objective functions, and application of the Pareto front method may have a large number of potential solutions. Selecting a feasible solution from such a large set is difficult to arrive the right solution for the decision maker. In this paper Pareto front ranking method is proposed to select the best parents for producing offspring’s necessary to generate the new populations sets in genetic algorithms. The bi-criteria objectives minimizing the machine idleness and penalty cost for scheduling process is solved using genetic algorithm based Pareto front ranking method. The algorithm is coded in Matlab, and simulations were carried out for the crossover probability of 0.6, 0.7, 0.8, and 0.9. The results obtained from the simulations are encouraging and consistent for a crossover probability of 0.6.