## 2 Literature Review
### Multi-Objective FSP under Uncertainty Conditions
\begin{table}
\begin{tabular}{p{85.4 pt} p{85.4 pt} p{85.4 pt} p{85.4 pt} p{85.4 pt} p{85.4 pt}} \hline \hline
**Reference** & **Objective Functions(s)** & **Problem Characteristics** & **Solution Approach** & **Remakes** \\ \hline \hline [PERSON] [43] & Total weighted turbines and total weighted flowtime. & Stochastic processing times and common dye dates. & Theorem. & Optimal solutions can be obtained by sequencing the jobs in increasing stochastic order of their processing times. Study other measures as the expected study jobs of overload variation in flowtimes. \\ \hline [PERSON] et al. [63] & Makkepun and maximum tardiness. & Fuzzy processing times and fuzzy due dates. & Genetic algorithm. & No recommendations given. \\ \hline [PERSON] and [PERSON] [64] & Fuzzy makespan, fuzzy maximum turbines, and fuzzy total flowtime. & Fuzzy processing times and fuzzy date dates. & Genetic algorithm. & The algorithm produces efficient solutions for medium- and large-sized problems in a reasonable amount of time. \\ \hline [PERSON] and [PERSON] [66] & Flow time and delay time of jobs. & Stochastic processing times and stochastic machine breakdowns. & Hybrid multi-objective genetic algorithm. & No recommendations given. \\ \hline [PERSON] et al. [66] & Makkepun and mean completion time. & Stochastic processing times, stochastic machine breakdowns, and stochastic setup times. & Artificial neural network. & The advantage of the proposed solution approach is the reduction in the number of simulations runs and, consequently, a reduced run time. Furthermore, this is the first study that introduced an intelligent and flexible algorithm for handling the stochastic two-machine's problem. \\ \hline [PERSON] et al. [67] & Makkepun and total hardness. & Stochastic processing times. & Evolutionary algorithm. & The authors demonstrated that an uncertainty-handling strategy is key to base option-goully solutions and that the aggregation’s performance is strongly related to the level of uncertainty about the environment. \\ \hline [PERSON] et al. [68] & Fuzzy makespan, flowtime, and toll tardiness. & Stochastic processing times and stochastic release times. & Chance constrained. & The genetic algorithm was allowed to resolve large instances with relatively good solutions in a reasonable computational time. \\ \hline [PERSON] et al. [69] & Hamming distance, adjustment of total completion times, and adjustments of processing times. & Inverse permutation flow shop scheduling problem and stochastic processing times. & Hybrid multi-objective evolutionary algorithm with the NEI-based insertion method. & The algorithm presents better results than a benchmark setting specific algorithm. The study can be extended by adding other measures. \\ \hline [PERSON] et al. [70] & Expected makespan and expected travellines. & Stochastic processing times with deteriorating and learning effects. & Multi-objective discrete fireworks algorithm. & The algorithm presents good results in comparison with the Miller model for small instances. \\ \hline [PERSON] et al. [16] & Expected tradiness and standard deviation of tradiness. & Stochastic processing times. & Simheuristic that hybridizes the Pareto archive evolution strategy with tables search. & The algorithm presents good results for the deterministic and stochastic the Pareto frontier of expected tradiness and standard deviation of tradiness. \\ \hline [PERSON] and [PERSON] [71] & Makkepun and energy consumption. & Stochastic processing times. & Mathematical formulation and scenario-based estimation of the distribution algorithm. & The algorithm obtains the Pareto frontier of the makespan and energy consumption and presents good results in comparison to another algorithm. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Modeling approaches for the multi-objective stochastic flow shop problem (MO-SFSP).