Bottleneck-based scheduling method of multi-robot cells with residency constraints

Wang, Zhu; Zhou, Binghai

doi:10.1080/0951192x.2014.964322

Cited by 8 publications

(2 citation statements)

References 30 publications

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“…Also in [296], the authors propose a software composition method for automated machines that exploits their mechatronic modularity, and they demonstrate that desired behavior of a certain class of machines can be composed of behaviors of its mechatronic components, including nonlinear process monitoring optimal operational indices selection locally weighted learning radial basis function networks recursive slow feature analysis M2M communication fully decentralized scheduling and operation control. Multiple machine job scheduling has been presented in [294], where the authors address the problem of scheduling multi-robot cells with residency constraints and multiple part types, in [299], where the authors consider the serial batching scheduling problem in which a group of machines can process multiple jobs continuously to reduce the processing times of the second and subsequent jobs, and in [300], where the authors study a twomachine scheduling problem in fuzzy environments. Multiple assembly lined scheduling is presented in [301], where the authors investigate robust order scheduling problems in the fashion industry by considering the preproduction events and the uncertainties in the daily production quantity.…”

Section: ) Anomalies Detectionmentioning

confidence: 99%

Data Management in Industry 4.0: State of the Art and Open Challenges

2019

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Information and communication technologies are permeating all aspects of industrial and manufacturing systems, expediting the generation of large volumes of industrial data. This article surveys the recent literature on data management as it applies to networked industrial environments and identifies several open research challenges for the future. As a first step, we extract important data properties (volume, variety, traffic, criticality) and identify the corresponding data enabling technologies of diverse fundamental industrial use cases, based on practical applications. Secondly, we provide a detailed outline of recent industrial architectural designs with respect to their data management philosophy (data presence, data coordination, data computation) and the extent of their distributiveness. Then, we conduct a holistic survey of the recent literature from which we derive a taxonomy of the latest advances on industrial data enabling technologies and data centric services, spanning all the way from the field level deep in the physical deployments, up to the cloud and applications level. Finally, motivated by the rich conclusions of this critical analysis, we identify interesting open challenges for future research. The concepts presented in this article thematically cover the largest part of the industrial automation pyramid layers. Our approach is multidisciplinary, as the selected publications were drawn from two fields; the communications, networking and computation field as well as the industrial, manufacturing and automation field. The article can help the readers to deeply understand how data management is currently applied in networked industrial environments, and select interesting open research opportunities to pursue.

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Section: ) Anomalies Detectionmentioning

confidence: 99%

Data Management in Industry 4.0: State of the Art and Open Challenges

2019

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“…Most existing scheduling methods, no matter in single-gripper robots [19][20][21][22][23] or in dual-gripper robots, [24][25][26] focus on either MP, a typical traditional process, where the machine processes one part individually, or RP where robots are used only for material handling. However, few studies pay attention to MRP in which robots concurrently perform collaborated tasks in addition to part transportation.…”

Section: Introductionmentioning

confidence: 99%

Scheduling method of robotic cells with machine–robot process and time window constraints

Zhou

2017

Proceedings of the Institution of Mechanical Engineers, Part E:

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Robotic cells are widely used in the fields of incorporate automation and repetitive processing. Throughput analysis and scheduling of robotic cells with machine process have been well studied, while research work on robotic cells with machine-robot process in which robots concurrently perform collaborated tasks in addition to part transportation is still at an early stage. After defining the concepts of internal and external time window constraints, we propose a robotic cell scheduling problem with machine process and machine-robot process simultaneously, when processing time window is an essential constraint. According to the constraints in real engineering practice, a mathematic model of single-gripper robotic cells is established to minimize the average manufacturing cycle time. A shifting bottleneck searching algorithm is proposed based on the basis of analyses. In addition, a lower bound for the average manufacturing cycle time is established. Finally, through extensive simulation experiments, the numerical and experimental results demonstrate that the shifting bottleneck searching provides optimal or near optimal solutions.

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A review of optimization models and applications in robotic manufacturing systems: Industry 4.0 and beyond

Vaisi

2022

Decision Analytics Journal

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Bottleneck-based scheduling method of multi-robot cells with residency constraints

Cited by 8 publications

References 30 publications

Data Management in Industry 4.0: State of the Art and Open Challenges

Data Management in Industry 4.0: State of the Art and Open Challenges

Scheduling method of robotic cells with machine–robot process and time window constraints

A review of optimization models and applications in robotic manufacturing systems: Industry 4.0 and beyond

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