## 2 Literature Review
[13] solved a novel DAPFSP by proposing a mixed integer linear model, three construction heuristics, two variable neighborhood search methods, and an iterative greedy algorithm to obtain important problem-specific knowledge to improve the effectiveness of the algorithm. Similarly, [PERSON] et al. [14] studied the DAPFSP with the objective of minimizing completion time and, proposed an iterative greedy based approach called bounded search iterative greedy algorithm. Similarly, in order to coordinate production and transportation scheduling, [PERSON] [15] proposed a novel DAPFSP-FABD model. The objective is to minimize the total cost of delivery and delay, and four heuristic algorithms, a variable neighborhood descent algorithm, and two iterative greedy algorithms are proposed. [PERSON] et al. [16] proposed a memory algorithm for DAPFSP based on variable neighborhood search with the objective function of, i.e., makespan. And, the initialization based on NEH heuristic is applied to the product ordering. The neighborhood structure is introduced into VNS and used to perturb the job assignment of the factory and adjust the job order of the factory. [PERSON] et al. [17] considered the DAPFSP with a total flow time criterion, and proposed an improved iterative greedy algorithm based on groupthink to solve the problem. It can be seen that the exact algorithm is no longer able to solve the problem, and this has promoted the development of heuristic and meta-heuristic algorithms, which can very definitely find the optimal solution as a solution to this scheduling problem. More and more scholars are focusing more on the improvement of algorithmic aspects. For the no-wait flow shop scheduling problem that depends on time series, [PERSON] et al. [18] proposed an enhanced differential evolutionary algorithm. Subsequently, [PERSON] [19] took minimizing the weighted sum of expected completion time and average completion time as the solution objective and proposed four metaheuristic algorithms; namely, genetic algorithm, imperialistic competition algorithm, cloud theory-based simulated annealing, and adaptive differential evolution algorithm. Moreover, [PERSON] et al. [20] argued that the imperialist competition algorithm can solve the fuzzy distributed assembly flow shop scheduling problem. Furthermore, [PERSON] et al. [21] proposed a multi-objective optimization model and particle swarm optimization solution method for the robust dynamic scheduling of permutation flow shops with uncertainty. Likewise, [PERSON] [22] emphasized that DAPFSP is a new generalization of the distributed displacement flow shop scheduling problem and the assembly flow shop scheduling problem, proposed an enhanced population-based metaheuristic genetic algorithm, and designed an effective crossover strategy based on local search to accelerate convergence. [PERSON] et al. [23] studied the minimization problem of DAPFSP and proposed a genetic algorithm with an enhanced crossover strategy and three different local searches. It is no coincidence that [PERSON] et al. [24] advocated an improved discrete artificial bee colony algorithm to solve the DAPFSP with preventive maintenance operator, optimized by the completion time criterion. Moreover, a local search method with insertion and exchange operators is used to generate adjacent solutions in the hiring bee phase and the bystander bee phase. [PERSON] and [PERSON] [25] jointly proposed a genetic programming hyperheuristic algorithm to solve the DAPFSP with sequence-dependent setup times, minimizing the completion time as the objective. For the improvement of the algorithm, these scholars further study the genetic algorithm and particle swarm algorithm. This also provides a reference basis for our subsequent simulation experiments.