  In this research we used bio-inspired metaheuristics, as artificial immune
systems and ant colony algorithms that are based on a number of characteristics
and behaviors of living things that are interesting in the computer science
area. This paper presents an evaluation of bio-inspired solutions to
combinatorial optimization problem, called the Job Shop Scheduling or planning
work, in a simple way the objective is to find a configuration or job stream
that has the least amount of time to be executed in machine settings. The
performance of the algorithms was characterized and evaluated for reference
instances of the job shop scheduling problem, comparing the quality of the
solutions obtained with respect to the best known solution of the most
effective methods. The solutions were evaluated in two aspects, first in
relation of quality of solutions, taking as reference the makespan and secondly
in relation of performance, taking the number evaluations performed by the
algorithm to obtain the best solution.
