A real-world scheduling problem using genetic algorithms

Genetic algorithms are an optimization methodology based on a direct analogy to Darwinian natural selection and mutations in biological systems. Instances of a concept description (such as schedules) correspond to individuals of a species, and induced changes and recombinations of these concepts are tested against an evaluation function (i.e., performance criteria) to see which alterations advance into the next generation. This genetic process encodes a parallel search through concept space that can be applied to solve job shop scheduling problems. A real-world scheduling example is presented illustrating the speed, responsiveness, flexibility, and computational efficiency of genetic algorithms to combinatorial optimization.