Evaluation of bioinspired algorithms for the solution of the job scheduling problem

TL;DR

This paper evaluates bio-inspired algorithms like artificial immune systems and ant colony optimization for solving the job shop scheduling problem, comparing their solution quality and performance against best known solutions.

Contribution

It provides an assessment of bio-inspired metaheuristics for job scheduling, highlighting their effectiveness and efficiency in finding near-optimal solutions.

Findings

Bio-inspired algorithms produce competitive solutions for job scheduling.

Performance varies with the number of evaluations needed.

Solutions are evaluated based on makespan and computational effort.

Abstract

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.