## 2 Literature Review  
### Related Works on FFSP  
For the FFSP, the current studies mainly focus on application background and optimization strategies. Optimization strategies vary with respect to different application scenarios and production requirements. Based on the count of scheduling objectives, studies on the FFSP can be further divided into two categories: single-objective FFSP and multi-objective FFSP. For the single-objective FFSP, researchers employ population-based intelligent algorithms, evolution-based intelligent algorithms and reinforcement learning algorithms as the optimization strategies. When dealing with the single-objective FFSP, minimizing the maximum completion time (i.e., makespan) is usually selected as the optimization objective. By considering the transportation time in a distributed manufacturing environment, [PERSON] et al. [5] established a distributed FFSP scheduling model aimed at minimizing the maximum completion time and proposed an improved cuckoo algorithm for solving the model. [PERSON] et al. [6] investigated an FFSP that encompassed multiple unrelated parallel machines (UPMs) for each process and considered the transportation time among processes. They constructed an integer programming model with the objective of minimizing the total weighted completion time and proposed an improved greedy genetic algorithm due to the model's NP-hard property. To address an FFSP involving limited buffers and two distinct process routes, [PERSON] et al. [7] proposed a discrete whale algorithm that integrated five variational operators and a deduplication strategy. To address the FFSP with UPM, [PERSON] et al. [8] proposed an enhanced gray wolf algorithm with new formulations for the key algorithmic control parameters. [PERSON] et al. [9] proposed a hybrid genetic algorithm combining Nawaz-Enscore-Ham (NEH) heuristic, local search and adaptive genetic algorithms for addressing an FFSP with UPM and batch production type. In this hybrid algorithm, NEH heuristic was used to generate initial solutions to improve the population quality. Owing to the potential and random occurrence of machine failures during the actual production process, [PERSON] et al. [10] employed the Hopfield neural network algorithm to simulate the annealing principle and the Nash bidding model to solve the FFSP. By comparing them with the genetic algorithm and the compact genetic algorithm on different instances, the effectiveness of the proposed algorithm was validated. In addition to makespan, other objectives (such as energy consumption, total delay time, and so on) are also simultaneously considered in the FFSP. When addressing such multi-objective FFSPs, researchers primarily employ population-based intelligent algorithms, evolution-based intelligent algorithms and human-behavior-based intelligent algorithms. [PERSON] et al. [11] investigated the two-stage FFSP in a circuit board discrete manufacturing system and developed a multi-objective optimization model aimed at minimizing both energy consumption and weighted delay time. [PERSON] et al. [12] regarded a capacitor discrete manufacturing shop as their research subject, constructed a multi-level FFS scheduling system architecture in an internet of things environment and designed an improved distribution estimation algorithm. [PERSON] et al. [13] proposed an improved ant colony optimization (ACO) algorithm for solving the FFSP with the objectives of maximizing the processing revenue and minimizing total carbon emission. The solution space is explored in depth by using three types of neighborhood structures to enhance the quality of the solutions. The adaptive construction probability was utilized to adjust the ant colony generation path. Considering that the production scheduling and process planning in a flexible flow shop were independent, [PERSON] et al. [PERSON] et al. (2014) developed a multi-objective optimization model aimed at minimizing the maximum completion time and the processing cost.