Mobile robot scheduling for cycle time optimization in flow-shop cells, a case study

Mosallaeipour, Sam; Nejad, Mazyar Ghadiri; Shavarani, Seyed Mahdi; Nazerian, Ramtin

doi:10.1007/s11740-017-0784-x

Cited by 29 publications

(12 citation statements)

References 15 publications

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“…AMRs are often small and more agile than AGVs. This implies that AMR can access more areas and be integrated to a higher degree in workspace or workstations, enabling manufacturing flexibility and meeting the current production demands (Mosallaeipour et al 2018). One application in the automotive sector indicates that AMRs can also be used as an assistive system, since they can interact with humans as a robotic co-workers in a wide variety of ways (Angerer et al 2012).…”

Section: Literature Reviewmentioning

confidence: 99%

Increasing flexibility and productivity in Industry 4.0 production networks with autonomous mobile robots and smart intralogistics

et al. 2020

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Manufacturing flexibility improves a firm's ability to react in timely manner to customer demands and to increase production system productivity without incurring excessive costs and expending an excessive amount of resources. The emerging technologies in the Industry 4.0 era, such as cloud operations or industrial Artificial Intelligence, allow for new flexible production systems. We develop and test an analytical model for a throughput analysis and use it to reveal the conditions under which the autonomous mobile robots (AMR)-based flexible production networks are more advantageous as compared to the traditional production lines. Using a circular loop among workstations and inter-operational buffers, our model allows congestion to be avoided by utilizing multiple crosses and analyzing both the flow and the load/unload phases. The sensitivity analysis shows that the cost of the AMRs and the number of shifts are the key factors in improving flexibility and productivity. The outcomes of this research promote a deeper understanding of the role of AMRs in Industry 4.0-based production networks and can be utilized by production planners to determine optimal configurations and the associated performance impact of the AMR-based production networks in as compared to the traditionally balanced lines. This study supports the decision-makers in how the AMR in production systems in process industry can improve manufacturing performance in terms of productivity, flexibility, and costs.

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Section: Literature Reviewmentioning

confidence: 99%

Increasing flexibility and productivity in Industry 4.0 production networks with autonomous mobile robots and smart intralogistics

et al. 2020

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“…There are several developments and implementation of Industrial Robots (IR) into production facilities for the material handling and the different processes applications. Such as IR for the measurement process, the integration of IR in a manufacturing cell for pick and place, also in welding process [12,13]. Conversely, there is still a need to do research and study regarding the usage and implementation of mobile robots for material handling in the production field, and how mobile robots can be combined with the industrial internet of things for effective decision-making and improvement of a transportation process.…”

Section: Autonomous Mobile Robot For Intralogisticsmentioning

confidence: 99%

Production Intralogistics Automation Based on 3D Simulation Analysis

Mahmood¹,

Karjust²,

Raamets³

2021

Journal of Machine Engineering

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Recent trends in manufacturing such as Industry 4.0 and Smart Manufacturing have brought the researchers' attention to the smart intralogistics in production facilities. Automated guided vehicles (AGV), especially mobile robots play a vital role in this development. On the other hand, industrial internet technologies offered new possibilities for the information exchange between devices, data integration platforms and communication interfaces to advance and facilitate the intralogistics for effective material handling and transportation. In order to analyse the feasibility and effectiveness of the mobile robots in the production area, 3D visualization should be combined with simulation, which provides a comprehensive possibility to evaluate and review the potential solution performance and its consistency before implementing practically into the production floor area. This paper describes a conceptual model based on 3D visualization and simulation and experimental study which help to make the decision according to the input data from the factory environment of the movement of mobile robots in production logistics. Moreover, the Key Performance Indicators (KPIs) are defined to analyse the use-case's process improvement in terms of the time reduction, which leads to increase productivity and cut-down the workers' fatigue.

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“…These strategies use algorithms to identify patterns in data and use that data to make decisions about how tasks should be scheduled. By combining data with advanced algorithms and computing power [7], machine learning algorithms can help organizations make better decisions faster and more accurately [8].…”

Section: Introductionmentioning

confidence: 99%

Robotic Scheduling Strategies based on Machine Learning Algorithm

Xing

et al. 2023

J. Phys.: Conf. Ser.

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Recently, robot scheduling has been widely concerned by numerous companies and researchers. They adopt an intelligent algorithm to design scheduling strategies for automated guided vehicle (AGV) and achieve required moving routes and certain aims. However, the existing robot scheduling strategies employ mathematical program models to arrange robots’ control and ignore machine issues that the robots may have. This will cause conflict and may lead the whole robot to a deadlock when the scale of conflict reaches a critical point. In this paper, we present a novel scheduling strategy for intelligent robots by training machine learning algorithms. Initially, we assume the robots contain a communication system, which can only receive signals from the central controller. Subsequently, we utilize the input parameters including robot conditions, working area, and working aims to train the machine learning algorithm. The work accomplishment ratio and conflict ratio are two essential indicators to measure our proposed method. Additionally, we also simulate the existing robot scheduling models to compare. From the extensive experimental results and comparison evaluations, we can conclude that our proposed method can achieve the robots scheduling tasks with reasonable costs. At last, we discuss the challenges of applying machine learning techniques to robotic scheduling and potential solutions and provide future directions in this field.

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Mobile robot scheduling for cycle time optimization in flow-shop cells, a case study

Cited by 29 publications

References 15 publications

Increasing flexibility and productivity in Industry 4.0 production networks with autonomous mobile robots and smart intralogistics

Increasing flexibility and productivity in Industry 4.0 production networks with autonomous mobile robots and smart intralogistics

Production Intralogistics Automation Based on 3D Simulation Analysis

Robotic Scheduling Strategies based on Machine Learning Algorithm

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