Design and simulation analysis of PDER: A multiple-load automated guided vehicle dispatching algorithm

Li, Maojia P.; Kühl, Michael

doi:10.1109/wsc.2017.8248048

Cited by 8 publications

(4 citation statements)

References 14 publications

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“…A workload-balancing algorithm is developed to deal with the pickup-dispatching problem that determines which job should be assigned to an AGV [38]. A multi-attribute dispatch approach was proposed based on vehicle-initiated task assignment by Jeong et al [39].…”

Section: Multi-attribute Strategies a Vehicle Initiated Task Assignme...mentioning

confidence: 99%

Analysis of Multi-AGVs Management System and Key Issues: A Review

Lu¹,

Guo²,

Song³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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Multiple Automatic Guided Vehicle (multi-AGVs) management systems provide an effective solution to ensuring stable operations of multi-AGVs in the same scenario, such as flexible manufacturing systems, warehouses, container terminals, etc. This type of systems need to balance the relationship among the resources of the system and solve the problems existing in the operation to make the system in line with the requirement of the administrator. The multi-AGVs management problem is a multi-objective, multi-constraint combinatorial optimization problem, which depends on the types of application scenarios. This article classifies and compares the research papers on multi-AGVs management in detail. Firstly, according to the different dimensions of the problem, the multi-AGVs management system is analyzed from three perspectives, namely, 1) task dimensiondispatch, 2) spatial dimension-path, and 3) time dimension-scheduling. The detailed comparison between the three dimensions and their respective solutions are discussed in detail as well. Secondly, according to their utility, the multi-AGVs management problems are divided into three categories: 1) cost reduction, resource-oriented, 2) efficiency improvement, problem-oriented, 3) personalized demand, goal-oriented. The algorithm and methods of the different utility-oriented are analyzed and discussed. The related literature is summarized and corresponds to the composition of the multi-AGVs management system and the multi-AGVs management problems. Finally, according to the literature review, suggestions are made for further research.

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Section: Multi-attribute Strategies a Vehicle Initiated Task Assignme...mentioning

confidence: 99%

Analysis of Multi-AGVs Management System and Key Issues: A Review

Lu¹,

Guo²,

Song³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

show abstract

“…The objective function is the makespan of jobs from raw material storage to finished parts storage. [16] consider the problem of dispatching multiple-load AGVs in an FMS. A PDER rule based on pickup-or-delivery-in-route is proposed to address the task determination problem, which indicates whether the next task of an AGV partially loaded should be picking up a new job or dropping off a carried load.…”

Section: A Related Workmentioning

confidence: 99%

Improved Particle Swarm Approach for Dynamic Automated Guided Vehicles Dispatching

Zaghdoud,

Amara,

Ghedira

2022

IJACSA

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The automated guided vehicles dispatching is one of the important operations in containers terminal because it affects the loading/unloading process. This operation has become faster and more complex until the automation advent. Although this evolution, the environment has become dynamic and uncertain. This paper aims to propose an improved particle swarm approach for solving the bi-objective problem of automated guided vehicles dispatching and routing in a dynamic environment of containers terminal. The objectives are to minimize the total travel distance of all automated guided vehicles and maximize the workload balance between them. The application of particle swarm algorithm in its basic form, shows a premature convergence. To ameliorate this convergence, the authors proposed the application of a method to escape the worst particles from the local optimum. The new Hybrid Guided Particle Swarm approach consists of hybridization between Dijkstra algorithms and a Guided Particle Swarm Algorithm. The routing problem is solved with Dijkstra algorithm and the dispatching problem with guided particle swarm approach. As a first step, this approach has been applied in a static environment where the dispatching parameters and the routing parameters are fixed in advance. The second step consists of applying this approach in a dynamic environment where the number of containers associated with each automated guided vehicles can change, the shortest path and the container locations can also change during the algorithm execution. The numeric results in a static environment show a good Hybrid Guided Particle Swarm performance with a faster and more stable convergence, which surpasses previous approaches such as Hybrid Genetic Approach and the efficiency of its extension approach Dynamic Hybrid Guided Particle Swarm in a dynamic environment.

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“…Simulation-based approaches exist to solve AGV path planning (Muller and Cardinal 2002), vehicle guiding (Duinkerken, ter Hoeven, and Lodewijks 2006), vehicle routing (Klaas, Laroque, Dangelmaier, and Fischer 2011;Faulin, Gilibert, Juan, Vilajosana, and Ruiz 2008), and scheduling (Wang and Zhou 2015) problems. However, only a few approaches exist for fleet capacity planning and vehicle dispatching (Ho and Chien 2006;Li and Kuhl 2017), especially assuming a highly complex and dynamic manufacturing environment with a large quantity of machines and uncertain parameters.…”

Section: Introduction and Motivation: Management Of Agv Fleets In Productionmentioning

confidence: 99%

Simulation-Based Digital Twin of a Complex Shop-Floor Logistics System

Gyulai

Bergmann

Lengyel

et al. 2020

2020 Winter Simulation Conference (WSC)

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Digital analytics tools have been at the forefront of innovation in manufacturing industry in recent years. To keep pace with the demands of industrial digitization, companies seek opportunities to streamline processes and enhance overall efficacy, opting to replace conventional engineering tools with data-driven models. In a high-tech factory, detailed data is collected about the products, processes, and assets in near-real time, providing a basis to build trustworthy analytical models. In this paper, a novel discrete-event simulation (DES) model is proposed for the detailed representation of a complex shop-floor logistics system, employing automated robotic vehicles (AGV). The simulation model is applied to test new AGV management policies, involving both vehicle capacity planning and dispatching decisions. In order to illustrate the usefulness of the model and the effectiveness of the selected policy, numerical results of a case-study are presented, in which the selected policy was realized in a real manufacturing environment.

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Design and simulation analysis of PDER: A multiple-load automated guided vehicle dispatching algorithm

Cited by 8 publications

References 14 publications

Analysis of Multi-AGVs Management System and Key Issues: A Review

Analysis of Multi-AGVs Management System and Key Issues: A Review

Improved Particle Swarm Approach for Dynamic Automated Guided Vehicles Dispatching

Simulation-Based Digital Twin of a Complex Shop-Floor Logistics System

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