Multi-Objective Evolutionary Job-Shop Scheduling Using Jumping Genes Genetic Algorithm

The job-shop scheduling problem (JSSP) is a hard combinatorial optimization problem. Several evolutionary approaches have been proposed to solve JSSP. But most of them are limited to single objective and fail in real-world applications, which naturally involve multiple objectives. In this paper, we pretend evolutionary approach for solving multi-objective JSSP using jumping genes genetic algorithm (JGGA) that heuristically searches for the near-optimal solutions optimizing multiple criteria simultaneously. Experimental results reveal that our proposed approach can search for the near-optimal solutions by optimizing multiple criteria and also capable of finding a set of diverse and nondominated scheduling solutions.