An estimate and simulation approach to determining the Automated Guided Vehicle fleet size in FMS

Yifei, Tao; Chen, Junruo; Liu, Meihong; Liu, Xianxi; Fu, Ya-li

doi:10.1109/iccsit.2010.5565147

Cited by 8 publications

(6 citation statements)

References 6 publications

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“…In the first stage, they estimated the required number of AGVs with the mathematical model and reduced the computation time, since they utilized the output as the simulation input. They reported that the fleet size estimation for multi-loaded AGVs with the heuristic method was not accurate when compared to real-life requirements; however, simulation methods led to a better solution [31].…”

Section: Literature Reviewmentioning

confidence: 99%

Determination of the Fleet Size of AGVs with AGV Pools Using a Genetic Algorithm and Artificial Intelligence

et al. 2023

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The utilization of low-cost AGVs in the industry is increasing every day, but the efficiency of these systems is low due to the lack of a central management system. Low-cost AGVs’ main characteristic is navigation via magnetic sensors, which they follow via magnetic tape on the ground with a low-level automation system. The disadvantages of these systems are mainly due to only one circuit assignment and the lack of system intelligence. Therefore, in this study, AGV pools were employed to determine the required AGV number. This study begins by calculating the required AGV number for each AGV circuit combination assigned to every parking station by the time window approach. Mathematical-solution-based mixed integer programming was developed to find the optimum solution. Computational difficulties were handled with the development of a genetic-algorithm-based approach to find the solutions for complex cases. If production requirements change, system parameters can be changed to adapt to the production requirements and there is a need to determine the number of AGVs. It was demonstrated that AGVs and pool combinations did not lead to any loss in production due to the lack of available AGVs. It was shown that the proposed approach provides a fleet size which requires five fewer AGVs, with a 29% reduction in the number of AGVs. The effects of system parameter changes were also investigated with artificial neural networks (ANNs) to estimate the required AGVs in the case of production requirement changes. It is necessary to determine the effect of the change in system parameters on the number of AGVs without compromising on computational cost and time, especially for complex systems. Thus, in this study, an artificial neural network (ANN), the response surface method (RSM), and multiple linear regression (MLR) techniques were used to examine the effects of the system parameter changes on the AGV number. In the present case, the ANN obtained the solution at a good rate with reduced computational costs, time, and correction errors compared to the GA, at 0.4% (ANN), 7% (RSM), and 24% (MLR). The results show that the ANN provides solutions which can be used in workshops to determine the number of AGVs and also to predict the effect of changes in system parameters.

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Section: Literature Reviewmentioning

confidence: 99%

Determination of the Fleet Size of AGVs with AGV Pools Using a Genetic Algorithm and Artificial Intelligence

et al. 2023

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“…The objective of their method was to minimize the travel time or overall cost. Yifei et al [37] developed analytical and simulation methods simultaneously to find the optimum number of AGVs in a flexible manufacturing system. Pjevcevic et al [38] designed a decision-making approach based on data envelopment analysis to determine the fleet size of AGVs at a port container terminal.…”

Section: Agv Fleet Sizing and Assignmentmentioning

confidence: 99%

An integrated automated guided vehicle design problem and preventive maintenance planning

Dehnavi-Arani

Hasani

2023

Soft Comput

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Nowadays, automated guided vehicles (AGVs) play a key role in manufacturing systems because of improving system efficiency and lowering the cost of production. To increase the efficiency and stability of AGVs, it is crucial to consider maintenance planning for them. To the best of our knowledge, it is rarely found studies related to maintenance planning and AGVs' design and control. Accordingly, in this paper, a new integrated nonlinear mathematical model is developed for optimizing the AGV design (including AGV fleet sizing and AGV assignment to workshops) and preventive maintenance policy. The proposed model aims to determine the preventive maintenance cycles, an optimal number of employed AGVs in manufacturing, and the optimal assignment of AGVs to manufacturing workshops so that the total cost is minimized. To solve this model, a genetic algorithm (GA) is developed, and their performance is compared with the global solver of LINGO software on 15 test problems, some of which are large dimensions. In order to tune the GA parameters, a Taguchi method is used. Moreover, a sensitivity analysis is performed to represent the validity of the model and solution approach. The results have demonstrated the effectiveness of GA in terms of computational time and solution quality.

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“…Other studies use two-stage approaches for system simulation and evaluation, or they develop case studies to determine fleet size and evaluate the impacts on indicators, such as queue sizes, occupation numbers, and service times [8,27]. In healthcare, simulation models are used to evaluate supply chain performance of AGV-based material handling systems versus manual delivery systems [22,21,2].…”

Section: Introductionmentioning

confidence: 99%

Simulation-Optimization of Automated Material Handling Systems in a Healthcare Facility

Bhosekar¹,

Isik²,

Ekşioğlu³

et al. 2020

Preprint

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Automated material handling systems are used in healthcare facilities to optimize material flow, minimize workforce requirements, reduce the risk of contamination, and reduce injuries. This study proposes a framework that integrates data analysis with system simulation and optimization to address the following research questions: (i)What are the implications of redesigning a hospitals material handling system? (ii)What are the implications of improving a hospitals material handling process? This paper develops a case study using data from the Greenville Memorial Hospital (GMH) in South Carolina, USA. The case study is focused on the delivery of surgical cases to operating rooms at GMH via Automated Guided Vehicles (AGVs). The data analysis provides distributions of travel times, AGV utilization, and AGV movement patterns in the current system. The results of data analysis are integrated in a simulationoptimization model that incorporates the size of AGV fleet and the corresponding routes to improve system efficiency, increase AGV utilization, and reduce congestion.To address research question (i), a redesign of AGV pathways is evaluated to determine whether congestion is reduced. For research question (ii), the implementation of a Kanban system is proposed to improve AGV utilization by controlling the number of AGVs used daily, based on the volume of surgical cases. An extensive sensitivity analysis, simulation-optimization experiments, and a pilot study are conducted and indicate that the proposed Kanban system leads to significant reductions in congestion and travel times and increased utilization of AGVs.

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An estimate and simulation approach to determining the Automated Guided Vehicle fleet size in FMS

Cited by 8 publications

References 6 publications

Determination of the Fleet Size of AGVs with AGV Pools Using a Genetic Algorithm and Artificial Intelligence

Determination of the Fleet Size of AGVs with AGV Pools Using a Genetic Algorithm and Artificial Intelligence

An integrated automated guided vehicle design problem and preventive maintenance planning

Simulation-Optimization of Automated Material Handling Systems in a Healthcare Facility

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