Evaluation of bioinspired algorithms for the solution of the job scheduling problem
This work addresses scheduling optimization for computational efficiency, but it is incremental as it applies existing bioinspired methods to a standard problem.
The paper evaluated bioinspired algorithms, such as artificial immune systems and ant colony algorithms, for solving the job shop scheduling problem to minimize execution time, comparing solution quality and performance against best-known methods.
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.