Multimodal processes prototyping subject to grid-like network and fuzzy operation time constraints

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.

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.

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 vehicles fleet match-up scheduling with production flow constraints

PurposeThis paper aims to propose and formulate a complicated routing/scheduling problem for multiple automated guided vehicles (AGVs) in a manufacturing system.Design/methodology/approachConsidering the due date of AGVs requiring for material handling among shops in a jobshop layout, their earliness and tardiness are significant in satisfying the expected cycle time and from an economic view point. Therefore, the authors propose a mathematical program to minimize the penalized earliness and tardiness for a conflict-free and just-in-time production.FindingsThe model considers a new concept of turning point for deadlock resolution. As the mathematical program is difficult to solve with a conventional method, an optimization method in two stages, namely, searching the solution space and finding optimal solutions are proposed. The performance of the proposed mathematical model is tested in a numerical example.Practical implicationsA case study in real industrial environment is conducted. The findings lead the decision-makers to develop a user interface decision support as a simulator to plan the AGVs’ movement through the manufacturing network and help AGVs to prevent deadlock trap or conflicts. The proposed decision support can easily be commercialized.Originality/valueThe benefits of such commercialization are increase in the quality of material handling, improve the delivery time and prevent delays, decrease the cost of traditional handling, capability of computerized planning and control, intelligent tracking and validation experiments in simulation environment.

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.

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.

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.

Industry 4.0: Sustainable material handling processes in industrial environments

Several manufacturing industries try to reduce transportation waste using automated material handling systems, which can enhance the transportation of raw materials from one location to another in the production line of a manufacturing area. The issue with transportation and job flow is a critical factor in a production line because some production stations need to wait for the work-in-progress to be delivered. Automated guided vehicle (AGV) transportation needs a setup of traffic control over a factory’s physical infrastructure and simulation. Doing so can help showcase and evaluate possible deficiencies that can be improved in the real job flow scenario of the production line. The design of experiment plays a huge role in finding and explaining variations of information under conditions that are regularly put as a hypothesis to reflect or describe the variation. A simulation model is implemented by adopting simplified AGV parameters. The model development follows the structure of system specification → machine specification → AGV specification → discrete-event simulation model → experimental design → analysis of performance indicators (PIs). To precisely reflect an alternative for evaluating aforementioned issues, this study proposes the model stated above and an analysis that is based on the PIs. Analysis of variance (ANOVA) results are chosen to analyze different factors affecting the PIs. Using the factorial ANOVA test results, this study uses one-way and two-way interactions to compare the relationship between job flow time, AGVs, AGV utilization, number of AGVs, and average waiting time.

Automated guided vehicles (AGVs) have been widely used in manufacturing and supply chain management for material handling. The efficiency of the material handling process has been the bottleneck of the production manufacturing. By applying maturing technologies such as sensing, cloud computing, and wireless communication, the efficiency and the reliability of the material delivery could be enhanced. In this paper, a cyber-physical system-based smart control model for shopfloor material handling is designed. In contrast to the traditional vehicle control methods, AGVs and base stations at intersections can communicate and interact with each other and share the real-time information online. Then, the smart control model, which consists of car-following model, overtaking model, and collision warning and avoidance model, is designed and developed. The presented model is demonstrated by a set of simulations and an experiment, which proved that the overall task completion efficiency and the utilization of the road are improved.

Scheduling of part and tools without considering material handling devices may result into non-implementable results as the material handling system constraints affect the overall scheduling. In this paper, an attempt has been made for the simultaneous scheduling of part, tool and two automated guided vehicles (AGVs) in a flexible manufacturing system which has identical machines with no on-board tool magazine. All the tools are stored in a common tool magazine and are shared among different machines through a material handling system. Each tool type is single in number. The problem has two interrelated problems; one as loading and scheduling of part and tool and other as scheduling of AGVs. Two heuristics namely priority dispatching rules (PDRs) and modified genetic algorithm (MGA) with three parent crossover are used to find the optimal solution to the integrated scheduling problem. Both PDRs and MGA use an algorithm based on the concept of Giffler and Thompson procedure and a heuristic approach to generate the active feasible schedule. The performance of the two proposed algorithm is compared for the results of makespan. It was found that the MGA gives better results.

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.

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.

Automated Guide Vehicles (AGVs) are widely used in material handling systems. In practice, to achieve more space utilization, safety, cost reduction, and increased flexibility, only a limited number of manufacturing cells may be preferred to have direct access to AGV travel paths, and the other cells are chosen to have no or indirect access to them. This paper investigates the problem of determining a single loop in a block layout with two criteria: loop length and loop-adjacency desirability. Unlike the traditional single shortest loop design problem, where all cells must be located next to the loop, the proposed problem considers a more realistic assumption that each cell in the block layout has a different preference with regard to being adjacent to the loop: some cells must be located adjacent to the loop, some must not be adjacent to the loop, and others can be located next to the loop but with different positive or negative priorities. The problem is formulated as a bi-objective integer linear programming model with two exponential-size constraint sets. A cutting-plane algorithm is proposed to solve the model under important methods commonly used to deal with a bi-objective model. The numerical results show the high efficiency of the proposed algorithm in large scales.