A Multiobjective Metaheuristic for Job-shop Scheduling

In this paper, we introduce a nature inspired meta-heuristic for scheduling jobs on computational grids. Our approach is to dynamically generate an optimal schedule so as to complete the tasks in a minimum period of time as well as utilizing the resources in an efficient way. The approach proposed is a variant of particle swarm optimization which uses mutation operator. The mutation operator can affect both particle’s personal best and the swarm’s global best. The experiments performed show the efficiency of the proposed approach over the standard PSO and other metaheuristics considered (namely genetic algorithms and simulated annealing).