## 2 Overview on AGVs and Related Problems
### Related Work
[21] address the AGV scheduling problem with pickup and delivery and present a novel multi-objective evolutionary algorithm to solve it. The goal is to maximize customer satisfaction while minimizing distribution cost. A multi-objective mixed-integer linear programming model is formulated. A set of computational experiments, based on real-world instances, are described. [PERSON] et al. [22] focus on the multi-objective scheduling of AGVs, where the aim is to allocate AGVs to tasks, minimizing makespan and number of AGVs while considering the AGVs' battery charge. A mathematical model is presented and integrated with different evolutionary algorithms: GA, PSO, and hybrid GA-PSO. According to the results of the computational experiments, the hybrid GA-PSO outperforms the other algorithms. Evaluation and validation of the approach is carried out by simulation via the FlexSim simulation software [23]. More recently, [PERSON] et al. [24] address the problem of scheduling transport requests on multi-load and multi-ability AGVs with battery management. Each request has a soft time window and a priority. The specific decisions involved are: assign transport and charging requests to AGVs, sequence them, and determine the arrival times and charging duration. The aim is to minimize the underlines costs of requests and travel costs of AGVs. The authors present a mixed-integer linear programming model and, as a solving methodology, a hybrid adaptive large neighborhood search. This approach is illustrated by means of an industry case study using real-world data. Finally, [PERSON] et al. [25] study the problem of scheduling AGVs with battery constraints. Each transport has an associated soft time window and each AGV has specific capabilities and travel costs. The AGV batteries can be recharged partially if required. The decisions involved in this problem are: to assign the transport and charging requests to AGVs, sequence the requests, and determine their starting times and recharging durations of the AGVs. The aim is to minimize a weighted sum of the tardiness costs of transport requests and travel costs of AGVs. The authors present a mixed-integer linear programming model and a novel matheuristic to address the problem. Finally, an industry case study is described. While most works rely on heuristics/metaheuristics [26], there are also some relying on reinforcement learning [27]. For instance, [PERSON] et al. [28] deal with a multi-AGV flow-shop scheduling problem, where each AGV moves along fixed tracks, transporting products between successive machines. As in many flow-shop scheduling problems, the usual goal is to minimize the average job delay and total makespan, for which many different types of algorithms can be employed [29; 30]. The authors formulate this scheduling problem as a Markov problem by defining state features, actions space and a reward function. Numerical experiments are carried out to assess the performance of the proposed approach. All in all, there is an increasing number of works studying the sequencing of tasks in AGV-related problems. Table 1 gathers the main features of the works related describedbefore. Most of these works describe challenging restrictions related to the use of batteries or the capabilities of the AGVs (e.g., capacitated multiple-load AGVs), or different objective functions. Many authors formulate a mathematical model and present a solving approach for a case study based on a real-life manufacturing enterprise. While the need to consider unexpected events and dynamic routing times has been acknowledged in some works, there is a gap in the literature regarding the use of simple yet efficient heuristics that feed from historical data and are capable to react, in real-time, to unexpected disruptions in the AGV paths.