Mathematical model for deadlock resolution in multiple AGV scheduling and routing network: a case study

Automated guided vehicles (AGVs) are widely used for transporting materials in industry and commerce. In this research, an intelligent AGV-based material-handling system was developed using a model- based systems engineering (MBSE) approach. The core of the AGV, the controller, was designed in the system modelling language environment using Visual Paradigm software, and then implemented in the hardware. As the result, the AGV’s complex tasks of material handling, navigation, and communication were successfully accomplished and tested in the real industrial environment. The developed AGV is capable of towing trolleys with a weight of up to 200kg at walking speed. The AGV can be incorporated into an intelligent material-handling system with multiple autonomous vehicles and work stations, thus providing flexibility and reconfigurability for the whole manufacturing system. Ergonomic and safety aspects were also considered in the design of the AGV. A comprehensive safety system that is compliant with industrial standards was implemented.

Current trend for Factory Automation is to design a variety of products to meet the changing demands of the customers. Automated material handling is an integral part of any automated manufacturing system. Automated Guided Vehicle (AGVs) is emerging as intelligent Material Handling Equipments (MHE) that is capable of adjusting to the changing scenario of manufacturing systems. AGVs are battery-powered, automated vehicles with capabilities to tag along programmed motions and orientation. AGVs selection has always been a challenging task owing to several constraints of manufacturing systems. Different MCDM (Multiple Criteria Decision Making) approaches have been employed by researchers for selection of MHE problem. This work is an attempt to choose amongst the AGVs alternatives and analyze their performance using certain MCDM approaches such as AHP, DEMATEL, TOPSIS, Fuzzy AHP, Fuzzy DEMATEL and Fuzzy TOPSIS. In the present work, data has been taken from Maniya and Bhatt [27] and the problem has been evaluated using four integrated approaches for analysis of AGV’s selection problem. It was found that Fuzzy AHP-Fuzzy TOPSIS and Fuzzy DEMATEL—Fuzzy TOPSIS technique gave better results for the instant case as they were devoid of vagueness error caused due to uncertainty in the decision making of experts. Authors have applied four approaches out of which DEMATEL-TOPSIS and Fuzzy DEMATEL-Fuzzy TOPSIS approaches has yet not been applied in AGVs selection problem, thus justifying original work and their appropriateness for the problem.

Material handling in manufacturing systems is becoming easier as the automated machine technology is improved. Nowadays, most of the research aims at increasing the flexibility and improving the performance of the automatic guided vehicle (AGV). In this paper, designed and made AGV in the Industrial Control Laboratory in Royce Lab at the University of Manchester Institute of Science and Technology will be presented. For controlling the navigation of the AGV, a newly developed controller integrated fuzzy logic with programmable logic controller will be used. By using integrated fuzzy logic controller with programmable logic controller (IFLPLC), the flexibility of AGV will be increased and we achieved great advantages, which can be used in future. Since this AGV uses programmable logic controller and fuzzy logic controllers together, then it will be useful for factories which implement flexible manufacturing system (FMS). Online maintenance and sending the commands to other machines from AGV and so on are the advantages that can be used in FMS. The aim of this paper is to propose the practical example of IFLPLC. The designed AGV is able to reach the target. Further researches are required for collision avoidance and selecting the best way in FMS

Automated guided vehicle dispatching and routing integration via digital twin with deep reinforcement learning

The initiative centers on the development and production of an Automated Guided Vehicle (AGV), designed to transform logistics across multiple industries, such as manufacturing, healthcare, and hospitality. As businesses increasingly aim to enhance their operational efficiency, automating transportation becomes vital for lowering labor expenses and minimizing the time delays associated with human involvement. The AGV presents considerable benefits in material handling, facilitating the effective and dependable movement of goods. Fitted with cutting edge sensors, the AGV can navigate autonomously within its surroundings, identifying and circumventing obstacles in real time. This feature guarantees safe functionality in dynamic environments, making it applicable in a variety of sectors. For example, in healthcare settings, the AGV can deliver medical supplies and equipment, reducing wait times and improving patient outcomes. In educational facilities, it can transport documents between offices, optimizing administrative workflows. The AGV’s adaptability also extends to shopping centers, hotels, banks, and corporate offices, where it can efficiently distribute items, thereby enhancing service delivery. Powered by a battery operated remote sensing system, the vehicle is designed to follow designated routes indicated by QR codes. This approach ensures precise navigation and simplifies the complexities often associated with traditional routing methods. By incorporating advanced technology, the AGV not only boosts operational effectiveness but also fosters a more organized and systematic logistics framework. In summary, this project is set to significantly influence various sectors by meeting the urgent demand for automation in material transport. As industries continue to progress and adopt technological innovations, the deployment of AGVs will be essential in optimizing operations, ensuring prompt deliveries, and ultimately enhancing overall productivity.

Automated guided vehicle (AGV) is widely used in automated manufacturing systems as a material handling tool. Although the task scheduling problem with isomorphic AGV has remained a very active research field through the years, too little work has been devoted to the task scheduling problems with heterogeneous AGVs. A coupled task with heterogeneous AGVs is a complex task that needs the cooperation of more than one type of AGVs. In this paper, a manufacturing system with two types of AGVs and three types of tasks is studied. To solve the coupled task scheduling problem with heterogeneous AGVs in this manufacturing system, we introduce two new methods based on the established mathematical model, namely, the decoupled scheduling strategy and coupled scheduling strategy with multi-decision model. The decoupled scheduling strategy is widely used in coupled task scheduling problems. However, there are some situations that the decoupled scheduling strategy cannot solve the problem well. To overcome the problem, the multi-decision point model solves the coupled task scheduling problem without decomposition. In order to ensure the searching speed and searching accuracy, a novel hybrid heuristic algorithm based on simulated annealing algorithm and tabu search algorithm is developed. The simulation experiment results show the proposed coupled scheduling algorithm has priority in coupled task scheduling problems.

Modern automated manufacturing processes employ Automated Guided Vehicle (AGV) for material handling, which serve several machine centres (MC) in a factory. Waiting time for resources such as AGVs are the longest elements that make up the Manufacturing Lead Time (MLT). Hence, optimal scheduling of AGVs can significantly help to increase the efficiency of the manufacturing process by minimizing the idle time of MCs waiting for the raw materials. In this paper, we will analyse the requirements for an optimal schedule and then provide a mathematical framework for an efficient schedule of material delivery by an AGV. The optimal schedule depends on several factors, such as the processing speeds of MCs, the speed and the material carrying capacity of the AGV, and system dependent overheads (such as loading and unloading time, machine set-up time, distance travelled, etc). A mathematical model is developed and then a strategy for optimal material distribution of the available raw material to the MCs is derived. With this model, the optimal number of MCs to be utilized will also be determined. Finally, the material delivery schedule employing multiple journeys to the MCs by the AGV will be carried out. Through rigorous analysis and simulation experiments, we shall show that such a delivery strategy will optimize the overall performance.

Agent technology has been considered as an important approach for developing intelligent manufacturing systems. It offers a new way of thinking about many of the classical problems in manufacturing engineering. The conflict resolution of automated guided vehicles (AGVs) in a flexible manufacturing systems (FMS) environment is one such problem that comes under this category. This paper describe a multi-agent approach to the operational control of AGVs by integration of path generation, enumerating time-windows, searching interruptions, adjusting waiting time and taking decisions on the selection of routes. It presents an efficient algorithm and rules for finding a conflict-free shortest-time path for AGVs, which is applicable to a bi- and unidirectional flow path network. The concept of loop formation in a flow path network is introduced to deal with the parking of idle vehicles, without obstructing the path of moveable AGVs. The concept of loop formation at nodes reduces the timing-taking task of finding the dynamic positioning of idle AGVs in the network.

Automatic grouping of production data in Industry 4.0: The use case of internal logistics systems based on Automated Guided Vehicles

This paper presents an example of optimization of an automated manufacturing system comprising a transport subsystem based on the automated guided vehicles (AGV). Number of resources in the manufacturing system and the number and location of the AGVs were subject of optimization. Profit maximization was assumed as an objective function. Arena and OptQuest software were used to build the simulation model and perform the optimization. The article describes an original solution for optimizing the number and cost of AGVs.

Automated Guided Vehicles (AGVs) equipped with robotic arms have gained significant attention as a transformative solution for improving efficiency and flexibility in material handling operations. This research paper delves into the integration of robotic arms into AGVs, providing an in -depth analysis of the benefits, challenges, and potential applications of this technology. The paper explores the underlying technologies, including navigation systems, perception sensors, and robotic arm control, that enable seamless collaboration between AGVs and robotic arms. It examines the advantages of AGVs with robotic arms across various industries such as manufacturing, warehousing, and logistics. Furthermore, the paper addresses safety considerations, cost implications, and future prospects of AGVs with robotic arms. The findings from this research provide valuable insights for businesses aiming to implement this cutting -edge technology to optimize their material handling processes.

Automated guided vehicles (AGVs) have long been identified as a potential driver to improve system efficiency and lower labour costs in material handling systems. Accordingly, the reliability and availability of AGV systems is crucial to assure the stability and efficiency of these systems. However, the reliability issues and maintenance strategies of AGVs have not previously been studied sufficiently. This is even more marked in the case of multi-AGV systems that consist of fleets of AGVs. To fill this knowledge gap, research is conducted considering a multi-AGV system, consisting of three AGVs, in order to develop a scientific methodology for optimising the layout design, operation and maintenance of a multi-AGV system. Once an AGV is failed, it will be towed to the maintenance site for repair by a recycle vehicle to prevent deadlock and conflict. The efficiency of the recycling process of failed AGVs in a multi-AGV system, with respect to the change of location of the maintenance site, is analysed by the approach of coloured Petri nets (CPNs). A CPN model simulating the corrective and periodic preventive maintenance processes of failed AGVs is also developed in order to investigate the impact of different AGV maintenance strategies on the operation efficiency of the multi-AGV system. The simulation results obtained clearly show that the location of maintenance sites and maintenance strategies do have significant influence on the performance of a multi-AGV system, where corrective maintenance is an effective measure to maintain the long-term reliability and stability of the system.

Pick up‐dispatching problem together with delivery‐dispatching problem of a multiple‐load automated guided vehicle (AGV) system have been studied. By mixing different pick up‐dispatching rules, several control strategies (alternatives) have been generated and the best control strategy has been determined considering some important criteria such as System Throughput (ST), Mean Flow Time of Parts (MFTP), Mean Tardiness of Parts (MFTP), AGV Idle Time (AGVIT), AGV Travel Full (AGVTF), AGV Travel Empty (AGVTE), AGV Load Time (AGVLT), AGV Unload Time (AGVUT), Mean Queue Length (MQL) and Mean Queue Waiting (MQW). For ranking the control strategies, a new framework based on MADM methods including fuzzy MADM and TOPSIS method were developed. Then several simulation experiments which had been based on a flow path layout to find the results were conducted. Finally, by using TOPSIS method, the control strategies were ranked. Furthermore, a similar approach was used for determining the optimal fleet size. The main contribution of this paper is developing a new approach combining the top managers′ views in selecting the best control strategy for AGV systems while trying to optimize the fleet size at the mean time by combining MADM, MCDM and simulation methods.

Industry 4.0: Sustainable material handling processes in industrial environments

Digital Twins are increasingly being introduced for smart manufacturing systems to improve the efficiency of the main disciplines of such systems. Formal techniques, such as graphs, are a common way of describing Digital Twin models, allowing broad types of tools to provide Digital Twin based services such as fault detection in production lines. Obtaining correct and complete formal Digital Twins of physical systems can be a complicated and time consuming process, particularly for manufacturing systems with plenty of physical objects and the associated manufacturing processes. Automatic generation of Digital Twins is an emerging research field and can reduce time and costs. In this paper, we focus on the generation of Digital Twins for flexible manufacturing systems with Automated Guided Vehicles (AGVs) on the factory floor. In particular, we propose an architectural framework and the associated design choices and software development tools that facilitate automatic generation of Digital Twins for AGVs. Specifically, the scope of the generated digital twins is controlling AGVs in the factory floor. To this end, we focus on different control levels of AGVs and utilize graph theory to generate the graph-based Digital Twin of the factory floor.