## 2 Literature Review
### Single-Objective SFSP  
Since the 1950s, the deterministic FSP has received considerable attention, but the SFSP has been less studied because it is more difficult to solve than the deterministic version. Nevertheless, with the advances in computation, the SFSP has been studied more in the last few years. Therefore, we present in chronological order the most important studies on the SFSP since its beginnings. From the early days of this topic until the 2010s, the following approaches have been found. [PERSON] [35] presented an optimal solution for the expected makespan in an SFSP with two jobs and general distributions for two machines, as well as with three machines, considering the exponential and Erlang distributions of the processing times. [PERSON] [36] proposed a rule to optimally sequence n jobs and two machines with exponentially distributed processing times, and this was proved optimal by [PERSON] and [PERSON] [37]. [PERSON] et al. [38] developed a dynamic procedure for considering stochastic machine breakdowns in an SFSP. This procedure can obtain an optimal solution for the initial stochastic problem when the associated without-breakdowns stochastic partial problems are solved optimally. [PERSON] et al. [39] proposed simulated annealing with a Markovian model to solve the SPFSP with stochastic processing times and limited buffers. [PERSON] et al. [40] presented a genetic ordinal optimization approach that hybridizes ordinal optimization, optimal computing budget allocation, and a genetic algorithm with uniformly distributed processing times. [PERSON] and [PERSON] [41] developed a new rule that enables optimal solutions when processing times are Weibull distributed. This rule includes [PERSON]'s and [PERSON]'s rules as special cases. [PERSON] and [PERSON] [42] proposed a heuristic called API that consists of two steps: (1) the ordering of jobs with expected processing times through [PERSON]'s rule and (2) applying all possible interchanges of two jobs in the sequence. Then, in the early 2010s, [PERSON] and [PERSON] [43] compared [PERSON]'s, [PERSON]'s, and the API rules, testing different types of probability distributions. The authors concluded that for a high coefficient of variation, [PERSON]'s and [PERSON]'s rules yield better results, but when the coefficients of variation are low, [PERSON]'s rule alone provides good results. [PERSON] and [PERSON] [44] compared three heuristic procedures adapted from rules that performed well in deterministic counterparts. The procedures used were CDS/[PERSON], CDS/Talwar, and NEH. The authors found that NEH had the best results. [PERSON] and [PERSON] [45] solved the SFSP considering normally distributed processing times and variances proportional to their means, as well as gamma distributed processing times with the same scale parameter for all jobs. The authors aimed to minimize the expected tardy jobs by using chance constraint programming. [PERSON] and [PERSON] [46] proposed a dynamic method to solve the SFSP in which the stochastic parameters were the due dates of jobs, in order to minimize the expected tardy jobs. [PERSON] et al. [18] developed a simheuristic that involves an iterated local search metaheuristic considering stochastic processing times distributed lognormally. [PERSON] and [PERSON] [28] compared well-known heuristics through a simulation procedure with a variable number of iterations and different probability distributions. The procedures compared were stochastic NEH, stochastic CDS/Talwar, deterministic NEH, deterministic CDS/Talwar, and deterministic NEH using CDS/Talwar as the initial solution. These authors found that stochastic NEH obtained the best results. Finally, in the late 2010s and the beginning of the 2020s, [PERSON] et al. [47] minimized the expected makespan in a distributed assembly PFSP with stochastic processing times through a GRASP simheuristic, evaluating the robustness of the problem as well. [PERSON] et al.