Job Scheduling in Multi Processor Architecture Using Genetic Algorithm

Job scheduling is an important issue which has many applications in different fields. In this paper job scheduling in multi processor architecture is studied. The main issue is how jobs are partitioned between processors in which total finishing time and waiting time are minimized. Minimization of these two criteria simultaneously, is a multi objective optimization problem. To solve this problem, a genetic algorithm is proposed to optimize two objectives simultaneously. In so doing, fitness function based on aggregation is used. In addition, longest processing time and shortest processing time algorithms are implemented to compare with genetic algorithm. Results of three methods are compared in unified condition simulation. Proposed genetic algorithm shows better results in experiments and can reduce finishing time and waiting time simultaneously.