## 1 Introduction  
The assignment and sequencing of jobs to production resources represent a complex process in most manufacturing companies, even more in flexible job shop systems where the production scheduling problem has been proved to be a nondeterministic polynomial time-hard problem (NP-hard problem) [1, 2], and for which optimal solution algorithms cannot provide solutions in reasonable computational times, especially for large and complex production plants. When considering the FJSSP, the concept of a set of machines in series is replaced by a set of work centers, where each work center contains a set of parallel machines that can have different processing times. In the FJSSP, each job follows a production route and can be processed on any machine that makes up a work center [3]. Moreover, the FJSSP requires a detailed discussion of the scheduling of orders importance from the point of due dates [4], implying the consideration of objective functions related to tardiness and earliness [5, 6]. However, many FJSSP models proposed in the literature assume conditions far from reality, ignoring the recirculation of jobs in work centers or machines and considering certainty in all the parameters used to perform the production schedule. In addition, the studies considering parameter uncertainty do not include variables such as setup times dependent on the operation sequence. In most scheduling models, processing time and due dates represent uncertain variables [7; 8; 9; 10; 11], and some studies use fuzzy logic theory to create a hybrid genetic algorithm to minimize makespan [12]. Furthermore, production scheduling problems optimizing earliness and tardiness have increased in relevance in recent years due to the growing interest in just-in-time production [13]. In realistic production scheduling problems, jobs finish as close as possible to due dates or within time-windows, due dates (due windows) since the early completion of jobs generates additional production and storage costs and inventory obsolescence; while late completion causes lost sales [5; 14]. Therefore, optimal scheduling minimizing earliness and tardiness penalties can improve the economic effects of firms. On the other hand, the complexity of the FJSSP requires the use of techniques that provide solutions in reasonable computational times to NP-hard problems. These solutions come from artificial intelligence techniques, which have been active in planning and scheduling for four decades [15]. Therefore, many heuristics and metaheuristics such as genetic algorithms, honey bee optimization, artificial bee colony, ant colony optimization, particle swarm optimization, simulated annealing, and hybrid approaches have been proposed to solve the FJSSP [16]. [PERSON] and [PERSON] [17] performed a review of techniques addressing the FJSSP, highlighting ant colony optimization (ACO), artificial bee colony (ABC), artificial immune system (AIS), evolutionary algorithms, greedy randomized adaptive search procedure (GRASP), Integer/Linear programming, neighborhood search (NS), particle swarm optimization (PSO), simulated annealing (SA), tabu search (TS), mathematical programming, deterministic heuristics, hybrid techniques, and miscellaneous techniques. Evolutionary algorithms represent the most used techniques in the literature to solve the FJSSP and cover a wide variety of techniques including Biogeography-based optimization (BBO), Differential evolution (DE), Evolution strategy (ES), Gene expression programming (GEP), Genetic Algorithms (GA), Genetic programming (GP), Harmony Search (HS), Learning classifier system (LCS), Memetic Algorithms (MA), and Estimation of distribution algorithm (EDA). These techniques are effective for minimizing the maximum completion time of the jobs, better known as makespan [18], and they can also assume other objectives, such as reducing delivery times, minimizing tardiness, minimizing earliness, minimizing resource costs, minimizing flow time, and minimizing the number of tardy jobs [15; 17].