A review of design and control of automated guided vehicle systems <s> Introduction <s> Abstract The interest on Automated Guided Vehicle Systems (AGVS) research has increased significantly in the last decade. Among the many issues associated with the design and planning of AGV systems, a key problem is the vehicle management problem. In this paper, we give a comprehensive review and discussion of the procedures proposed in the literature. <s> BIB001 </s> A review of design and control of automated guided vehicle systems <s> Introduction <s> Abstract Automated guided-vehicle (AGV) systems are gaining increasing acceptance in modern manufacturing facilities primarily because of the versatility they offer. As the flexibility and complexity of these systems increase, the requirements of the design effort and the routeing and scheduling system grow. In this paper we provide a review of the literature relevant to the system design, routeing and scheduling, and justification and implementation of AGV systems. <s> BIB002 </s> A review of design and control of automated guided vehicle systems <s> Introduction <s> The effectiveness of a material handling system depends on several factors, among them a well-thought-out flow-path design. The flow path has a significant effect on the travel time, the operating expenses, and the installation costs of the system. Moreover, the flow-path configuration has a significant impact on the complexity of the system's control software. The literature review presented in this paper describes several approaches to the design of material flow networks, including conventional type systems and more recent developments like the single-hoop, tandem configuration, SBSL, and SFT. <s> BIB003 </s> A review of design and control of automated guided vehicle systems <s> Introduction <s> Automated guided vehicles (AGVs) are now becoming popular in automated materials handling systems, flexible manufacturing systems and even container handling applications. In the past few decades, much research has been devoted to the technology of AGV systems and rapid progress has been witnessed. As one of the enabling technologies, scheduling and routing of AGVs have attracted considerable attention. Many algorithms for the scheduling and routing of AGVs have been proposed. However, most of the existing results are applicable to systems with a small number of AGVs, offering a low degree of concurrency. With a drastically increased number of AGVs in recent applications (e.g. in the order of a hundred in a container handling system), efficient algorithms are needed to resolve the increased contention of resources (e.g. path, loading and unloading buffers) among AGVs. This survey paper first gives an account of the emergence of the problem of AGV scheduling and routing. It then differentiates it from seve... <s> BIB004 </s> A review of design and control of automated guided vehicle systems <s> Introduction <s> Guided vehicles (GVs) are commonly used for the internal transportation of loads in warehouses, production plants and terminals. These guided vehicles can be routed with a variety of vehicle dispatching rules in an attempt to meet performance criteria such as minimizing the average load waiting times. In this research, we use simulation models of three companies to evaluate the performance of several real-time vehicle dispatching rules, in part described in the literature. It appears that there is a clear difference in average load waiting time between the different dispatching rules in the different environments. Simple rules, based on load and vehicle proximity (distance-based) perform best for all cases. The penalty for this is a relatively high maximum load waiting time. A distance-based rule with time truncation, giving more priority to loads that have to wait longer than a time threshold, appears to yield the best possible overall performance. A rule that particularly considers load-waiting time performs poor overall. We also show that using little pre-arrival information of loads leads to a significant improvement in the performance of the dispatching rules without changing their performance ranking. <s> BIB005
Material handling systems using automated guided vehicles (AGVs) are commonly used in facilities such as manufacturing plants, warehouses, distribution centers and terminals. Figure   1 gives an example of such a material handling system in a distribution center of computer hard and software BIB005 , in which guided vehicles transport pallets (loads) between locations, e.g. from receiving lanes to storage areas, and from storage areas to shipping lanes. The performance of the material handling system directly affects the performance of the whole facility. Basically, the relevant issues at facilities using automated guided vehicles can be divided into the following main categories: guide-path design, estimating the required number of vehicles, vehicle scheduling, idle-vehicle positioning, battery management, vehicle routing and conflict resolution. These issues relate to different levels of the decisionmaking process. The guide-path design problem can be seen as a problem at strategic level. The decision at this stage has a strong effect on decisions at other levels. Issues at tactical level include estimating the number of vehicles, scheduling vehicle, positioning idle vehicles and, managing battery-charging scheme. Finally, the vehicle routing and conflict resolution problems are addressed at the operational level. During the design and implementation process, some interactions and iterations can be seen between steps. The type of the guidepath system directly influences the number of vehicles required and the complexity of the vehicle scheduling system. The scheduling system also affects the number of vehicles required. Modern AGV systems differ from the classic ones as described for instance in the books of Jünemann and Schmidt (2000) and  in several respects. Rather than using fixed paths, many modern AGVs are free-ranging, which means their preferred tracks are software programmed, and can be changed relatively easy when new stations or flows are added. A second difference is in the way they can be controlled. Agent technology allows decisions to be taken by these smart vehicles that in the past were taken by central controllers. This leads to adaptive, self-learning systems and is particularly appropriate for large, complex systems with many vehicles and much potential vehicle interference. These developments do not imply that the traditional decision-making problems become obsolete. Rather, they lead to new challenges for research. We both discuss the traditional AGV system decision-making problems and the impact of these developments on decision-making. There are few review papers on AGV systems. However, they concentrate on only limited parts of the problem BIB004 focus on scheduling and routing problems) or are not up to date BIB003 BIB001 BIB002 . Moreover, they ignore some areas such as idle-vehicle positioning and battery management. This paper attempts to fill this gap, by giving an extended overview of existing literature, including the most recent contributions, and also structures the design and implementation decision process for AGV systems. For each area, we review and classify key decision models. In addition, a new classification for dispatching rules, a guideline for selecting a suitable scheduling system and a decision framework for design and implementation of AGV systems are proposed. We also consider idle-vehicle positioning and battery management problem, which many review papers neglect. Finally, we make suggestions for some fruitful future research directions. We start with an introduction to AGV systems followed by a discussion of guide-path design in section 2. Section 3 investigates the problem of determining the required number of singleor multi-load vehicles. Section 4 reviews the problem of managing an AGV system including online and offline scheduling, vehicle dispatching, idle-vehicle positioning, battery management, vehicle routing and conflict resolution. In section 5, we illustrate the design and implementation decision process of an AGV system by a decision framework. Finally, in section 6 we draw conclusions and identify subjects for further research.