Chromosome Encoding Schemes in Genetic Algorithms for the Flexible Job Shop Scheduling: A State-of-art Review Useful for Artificial Intelligence Applications

This paper undertakes an innovative review and organization of the relevant issues of the FJSP in the genetic algorithm to provide some systematic way of organizing its issues and provide useful insights in this method of the genetic algorithm Flexible Job-shop Scheduling Problem (FJSP) is a type of scheduling problem with a wide range of application backgrounds. In recent years, genetic algorithms have become one of the most popular algorithms for solving FJSP problems and have attracted widespread attention. In this paper, a comprehensive review of chromosome coding methods of the genetic algorithm for solving the FJSP and three standards are used to compare the advantages and disadvantages of each coding method. The results show that MSOS-I coding is a better chromosomal encoding method for solving FJSP problems, whose chromosome structure is simple, feasibility and larger storage. The main contribution of this paper is to fill the literature gap, because No such comprehensive review of the FJSP in the GA prevails in the existing literature. This comprehensive review will be useful for scholars and practical applications of the FJSP and the genetic algorithm for artificial intelligence and machine learning implementations and applications.