Evolutionary algorithms for job shop scheduling

Abstract

Job Shop Scheduling Problem (JSSP) represents a real challenge for the researchers' community due to its complexity consisting in the plurality of resources that needs to be optimally used and the variety of goals that needs to be accomplished. This paper presents the implementation of three Evolutionary Algorithms (Genetic Algorithms, Particle Swarm Optimization and Ant Colony Optimization) for the JSSP. The tests are made considered a set of classical benchmarks for the proposed problem and the obtained results are subject to comparison.