Abstract: Multiprocessor task scheduling problem for dependent
and independent tasks is computationally complex problem. Many
methods are proposed to achieve optimal running time. As the
multiprocessor task scheduling is NP hard in nature, therefore, many
heuristics are proposed which have improved the makespan of the
problem. But due to problem specific nature, the heuristic method
which provide best results for one problem, might not provide good
results for another problem. So, Simulated Annealing which is meta
heuristic approach is considered. It can be applied on all types of
problems. However, due to many runs, meta heuristic approach takes
large computation time. Hence, the hybrid approach is proposed by
combining the Duplication Scheduling Heuristic and Simulated
Annealing (SA) and the makespan results of Simple Simulated
Annealing and Hybrid approach are analyzed.
Abstract: Multiprocessor task scheduling is a NP-hard problem and Genetic Algorithm (GA) has been revealed as an excellent technique for finding an optimal solution. In the past, several methods have been considered for the solution of this problem based on GAs. But, all these methods consider single criteria and in the present work, minimization of the bi-criteria multiprocessor task scheduling problem has been considered which includes weighted sum of makespan & total completion time. Efficiency and effectiveness of genetic algorithm can be achieved by optimization of its different parameters such as crossover, mutation, crossover probability, selection function etc. The effects of GA parameters on minimization of bi-criteria fitness function and subsequent setting of parameters have been accomplished by central composite design (CCD) approach of response surface methodology (RSM) of Design of Experiments. The experiments have been performed with different levels of GA parameters and analysis of variance has been performed for significant parameters for minimisation of makespan and total completion time simultaneously.