Dynamic seed genetic algorithm to solve job shop scheduling problems

Abstract

This paper proposes a simple implementation of genetic algorithm with dynamic seed to solve deterministic job shop scheduling problems. The proposed methodology relies on a simple indirect binary representation of the chromosome and simple genetic operators (one-point crossover and bit-flip mutation), and it works by changing a seed that generates a solution from time to time, initially defined by the original sequencing of the problem addressed, and then adopting the best individual from the past runs of the GA as the seed for the next runs. The methodology was compared to three different approaches found in recent researches, and its results demonstrate that despite not finding the best results, the methodology, while being easy to be implemented, has its value and can be a starting point to more researches, combining it with other heuristics methods that rely in GA and other evolutionary algorithms as well.