A new solution encoding scheme for solving the Flexible Job-Shop Scheduling Problem.

The Flexible Job-Shop Scheduling Problem is one of the most studied problems in the specialized literature. Since it was put forth for the first time, there have been multiple variants and constraints proposed in addition to the classic implementation. As an NP-Hard problem, the best way to solve the FJSP is through heuristic algorithms. In recent years there has been an increasing interest in solving NP-Hard problems by using Genetic Algorithms and the Flexible Job-Shop Problem is not an exception. There are different approaches based on a GA for solving the FJSP. This paper revisits the research on solution representation and, aiming to solve the FJSP, proposes a new encoding scheme that represents each solution as a sequencing paired list. The proposed scheme aims to improve the performance of a genetic algorithm toward better solutions within the search space. The encoding was tested on benchmarks commonly found in the literature. The experimental results show that this encoding scheme fulfills its objective and can be used as an alternative to other widespread encodings such as MSOS (Machine Selection and Operation Sequence).