Reducing cost of material handling has been a big challenge for companies. Flexible manufacturing system employed automated guided vehicles (AGV) to maintain efficiency and flexibility. This paper presents a new non-linear mathematical programming model to group n machines into N loops, to make an efficient configuration for AGV system in Tandem layout. The model minimizes both inter-loop, intra-loop flow and use balanced-loops strategy to balance workload in system simultaneously. This paper significantly considers multiple-load AGVs, which has capability of reducing fleet size and waiting time of works. A modified variable neighborhood search method is applied for large size problems, which has good accuracy for small and medium size problems. The results indicate that using multiple load AGV instead of single load AGV will reduce system penalty cost up to 44%.

http://www.qjie.ir/article_539592_353c26e8d8caa45b5221aea2f37f0c92.pdf

A Mathematical Model for Multiple-Load AGVs in Tandem Layout

Pointed at a problem that leads to the high complexity of the production management tasks in the multi-stage spinning industry, mixed flow batch production is often the case in response to a customer’s personalized demands. Manual handling cans have a large number of tasks, and there is a long turnover period in their semi-finished products. A novel heuristic research was conducted that considered mixed-flow shop scheduling problems with automated guided vehicle (AGV) distribution and path planning to prevent conflict and deadlock by optimizing distribution efficiency and improving the automation degree of can distribution in a draw-out workshop. In this paper, a cross-region shared resource pool and an inter-regional independent resource pool, two AGV predictive scheduling strategies are established for the ring-spinning combing process. Besides completion time, AGV utilization rate and unit AGV time also analyzed with the bottleneck process of the production line. The results of the optimal computational experiment prove that a draw frame equipped with multi-AGV and coordinated scheduling optimization will significantly improve the efficiency of can distribution. Flow-shop predictive modeling for multi-AGV resources is scarce in the literature, even though this modeling also produces, for each AGV, a control mode and, if essential, a preventive maintenance plan.

Flow-Shop Predictive Modeling for Multi-Automated Guided Vehicles Scheduling in Smart Spinning Cyber–Physical Production Systems

In the current industrial fields, automatic guided vehicles (AGVs) are widely employed to constitute the flexible manufacturing system (FMS), owing to their great advantages of routing flexibility and high efficiency. However, one main challenge lies in the coupling process of the design problem of the unidirectional guide-path network (UGN) and the task scheduling problem of AGVs. To reduce the complexity, most pertinent literatures only handle these problems one by one, based on the stepwise design methods, thereby neglecting the constraint conditions and the optimization objectives caused by the FMS environment. The motivation of the paper is to bring the coupling factors into the integrated design and solution process. Firstly, an integrated design model of designing UGN and scheduling AGVs simultaneously is proposed, with the objective of minimizing the makespan (i.e., the maximum completion time of all handling tasks), in the consideration of the practical constraints, e.g., the job handling and processing sequence constraints and the AGV number constraint. Secondly, a dual-population collaborative evolutionary genetic algorithm (CEGA) is developed to solve the problems of designing and scheduling in a parallel way. The solutions of the integrated model, i.e., the potential strongly connected UGN and the feasible processing and handling sequence, are, respectively, coded as two different subpopulations with independent and concurrent evolution processes. The neighbourhood search operation, the niche technique, and the elitism strategy are combined to improve the convergence speed and maintain the population diversity. The experimental results show that the integrated design model can formulate the problem more accurately, and the CEGA algorithm is computationally efficient with high solution quality.

/pdf/a-cega-based-optimization-approach-for-integrated-designing-4frjsw67h2.pdf

A CEGA-Based Optimization Approach for Integrated Designing of a Unidirectional Guide-Path Network and Scheduling of AGVs

A review of the state of the art is presented of the autonomous guidance vehicles (AGV) to transport objects and raw material in industrial applications. The literature review is focus in different topics as the identification of place, application in different environments, vehicle control and description of the software systems used for the programming of mobile robots, according to the requirements or needs to be resolved. Additionally, an analysis is made, based on the aforementioned references, of the contribution made by this type of technologies not only to the industry, but also to other types of business model, such as the health area, the systems of urban transport, etc. Se presenta una revision del estado del arte de vehiculos de guiado autonomo (AGV por sus siglas en ingles:  Automated Guided Vehicle ) para el transporte de objetos y materia prima en aplicaciones industriales. La revision de literatura hace enfasis en varios aspectos como la identificacion del lugar, aplicacion en diferentes entornos, control del vehiculo y descripcion de los sistemas de software usados para la programacion de los robots moviles, de acuerdo a los requisitos o necesidades a resolver. Adicionalmente, se hace un analisis, con base a las referencias citadas, del aporte que hace este tipo de tecnologias no solo a la industria, sino tambien a otros tipos de modelo de negocio, como lo son el area de la salud, los sistemas de transporte urbano, entre otros.

/pdf/vehiculos-de-guiado-autonomo-agv-en-aplicaciones-1awatcquwa.pdf

Vehículos de guiado autónomo (agv) en aplicaciones industriales: una revisión

Due to the advantages of high efficiency and flexibility, the Automatic Guided Vehicle System (AGVS) has been more and more widely used in many industries. However, one of the main challenges in AGVS control is how to prevent collision and deadlock between vehicles. Although many collision and deadlock prevention algorithms have been proposed, they are inefficient for the AGVS based on a unidirectional guide-path network (UGN). In order to solve this problem, this paper proposes a collision and deadlock prevention method with a traffic sequence optimization strategy for the UGN-based AGVS. First, a vehicle coordination method based on the semaphore theory and Internet of Things (IoT) positioning technology is proposed to prevent collisions and intersection congestion deadlocks. Then, to avoid cycle deadlocks, a cycle deadlock search and avoid algorithm based on the digraph theory is developed. After that a bidding mechanism-based strategy is developed to optimize the vehicle traffic sequence in each path intersection. Finally, extensive simulation tests are conducted to verify the performance of the proposed methods and strategy. Simulation results show that, compared to the zone controlled (ZC) methods, the average travel time of the proposed methods is reduced by 6.2%–29.6%, and the average throughput is increased by 3.5%–27.4%. Also, the bidding mechanism-based traffic sequence optimization strategy can not only increase the average throughput of the processing subsystem but also reduce the congestion and deadlock risk of the logistics transportation subsystem. The proposed method is suitable for an UGN-based AGVS in the manufacturing application environment.

/pdf/a-collision-and-deadlock-prevention-method-with-traffic-3hxyt90qq2.pdf

Varun Sai Chinthala

Papers

A Review On Facility Layout Design Of An Automated Guided Vehicle In Flexible Manufacturing System