## 2 Literature Review
### Related Works on AGV Scheduling Problem  
As mentioned above, an AGV is a driverless transportation vehicle that is widely used for material transportation and controlled by computers. Related studies on AGV scheduling problem usually focus on specific application scenarios, such as discrete manufacturing system or logistics. For instance, [PERSON] et al. (2019) added an induced factor to the state transfer rule to guide AGVs to avoid conflicts while optimizing the paths and waiting times and proposed an induced ant colony particle algorithm to solve the conflict problem in the scheduling of multiple AGVs. [PERSON] et al. [PERSON] et al. (2019) proposed a multi-AGV scheduling method based on an improved genetic algorithm, which can reduce the overall path distance of all AGVs, as well as the longest path distance of a single AGV. [PERSON] et al. (2019) utilized FlexSim simulation to gradually increase the number of AGVs, examined the hourly and average utilization rates of AGVs under different design schemes and developed an AGV configuration scheme to meet the actual production requirement.  
In recent years, with the widespread dissemination of the concept of intelligent manufacturing, AGVs have been increasingly applied to material transportation in manufacturing shops due to the outstanding features of easy operation, rapid response and high efficiency. Many scholars adopted intelligent optimization algorithms, such as the genetic algorithm, pollen propagation algorithm, particle swarm algorithm, and so on, to solve the shop scheduling problems with AGVs additionally involved in the transportation link. [PERSON] et al. [PERSON] et al. (2019) synchronized the three processes of production, transportation and warehousing for optimization, set the coupling term when building the model and used the objective cascade method. [PERSON] et al. [PERSON] et al. (2019) studied the joint scheduling problem of machines and AGVs in a flexible manufacturing system and built a MILP model with the objective of minimizing the makespan. A tabu search algorithm was designed as the solution strategy, and simulation experiments showed that the algorithm could obtain an optimal solution within reasonable time. [PERSON] et al. [PERSON] et al. (2019) studied assembly line balancing and the AGV transportation problem and proposed a layered planning approach with the objective of minimizing the processing time and the delay time of AGV transportation. A particle swarm algorithm was used in the first layer for solving the mixed-integer programming model of the assembly line balancing sub-problem. The obtained task sequencing and start times were input into the second layer in which a heuristic was designed for solving the AGV transportation sub-problem. [PERSON] et al. [PERSON] et al. (2018) designed an improved whale optimization algorithm that incorporated population initialization, crossover and mutation operators for the integrated scheduling problem of AGVs and flexible job shops. The improved algorithm used a process-sequencing-machine selection-based encoding scheme and a first-come-first-served-based AGV scheduling strategy. [PERSON] et al. [PERSON] et al. (2019) considered an integrated AGV job shop scheduling problem and constructed a bi-objective scheduling model that took cycle time and AGV utilization rate as the optimization objectives. Its shortcoming was that the count of AGVs was small, and it did not consider emergency issues, such as equipment failure, AGV conflicts, AGV congestion, and so on. [PERSON] et al. [PERSON] et al. (2019) studied the integrated scheduling problem of FFS production and AGV transportation and proposed an improved genetic algorithm based on the tabu search algorithm. Its shortcoming was that a fixed heuristic rule was chosen for task allocation, and the initial population was randomly generated.