# Hybrid Task Allocation of an AGV System for Task Groups of an Assembly Line
## 2 Related Work  
According to the number of AGVs required by a task, tasks can be divided into single-AGV tasks and multi-AGV tasks. At present, the research mainly focuses on the task allocation problem of single-AGV tasks. When solving the task allocation problem involving multi-AGV tasks, the completeness of materials and the punctuality of material delivery should be considered. In this section, we review literature closely related to the problem under consideration.  
For the task allocation problem of single-AGV tasks, many methods have been proposed. Some methods aimed at decreasing the makespan, such as a method integrated with several evolutionary algorithms [13; 14] and Giffler and Thompson algorithms with different priority dispatching rules [15]. Some methods aimed at avoiding deadlock [16; 17], minimizing the total tardiness of tasks [18], or minimizing the overall energy consumption [19], etc.  
The task allocation of multi-AGV tasks is a multi-object task assignment problem. As for the multi-object task assignment problem, studies are mainly distributed in the field of ship dispatching and robot scheduling. To ship dispatching, [PERSON] et al. [20] proposed a genetic ant colony hybrid algorithm to improve task allocation efficiency. For robot scheduling, [PERSON] et al. [21] studied the dynamic task assignment strategy for heterogeneous multi-robot teams. [PERSON] et al. [22; [PERSON] et al. 2020] proposed a subpopulation-based Genetic Algorithm (GA) and a novel memetic algorithm. [PERSON] et al. [24] proposed a local improvement heuristic based on a neighborhood operator. [PERSON] et al. [25] presented a new approach based on negotiation and priority rules.  
Regarding the completeness of materials, [26; 27; 28] considered that all materials for an operation could be packaged into one task. [PERSON] et al. [29] considered that each station involved material-handling tasks and tool-handling tasks. [PERSON] et al. [30] divided materials required by each station into one or multiple tasks, according to AGV capacity. However, the research above regards material handling tasks of one operation as one or more independent tasks, without considering the cooperative relationship between tasks. When multiple tasks in a task group are late, the operating delay time calculated by the literature above is bigger than the practical value.  
Concerning the punctuality of material delivery, there have been many studies. [PERSON] et al. [31] proposed a dynamic time estimation based on the AGV dispatching algorithm. [PERSON] et al. [32] introduced the design of the handshake. [PERSON] et al. [33] optimized the combination of rules based on the trained network. However, the studies above only required tasks to be delivered before the end time, without considering the problem that the material cannot be unloaded earlier than the start time of the corresponding task.  
Particle swarm optimization (PSO) is one of the most prevalent intelligence algorithms. Due to its fewer parameters, ease of implementation and strong competitiveness compared with other swarm intelligence algorithms, many researchers have employed PSO to solve task allocation problems. To mention a few, [PERSON] et al. [34] presented A fuzzy hybrid GA-PSO algorithm for multi-objective AGV scheduling in a flexible manufacturing system (FMS). [PERSON] et al. [35] used a hybrid PSO to settle the AGV scheduling problems at container terminals. A hybrid GA-PSO was applied for AGV scheduling problems in FMS in [14]. In terms of the excellent achievements of these papers, we hope to exploit a PSO variant for the problem under consideration.  
From the above brief review, we can see that task allocation is a very active research field. Moreover, the existing research has a strong reference value for us to solve the hybrid task allocation problem considered in this paper. To the best of our knowledge, this is the first study to account for the cooperative relationship between tasks in the hybrid task allocation problem.