Integrated automation system with PSO based scheduling for PCB remanufacturing plants

Brusaferri, Alessandro; Leo, Egidio; Nicolosi, Leonardo; Ramin, Danial; Spinelli, Stefano

doi:10.1109/indin41052.2019.8972118

Cited by 4 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed approach has been tested with the application to the STIIMA-CNR pilot plant for remanufacturing of mechatronic products [21]. We focus on the flexible conveyor system, aimed to move the of the Printed Circuit Boards (PCBs) across the operating stations (e.g.…”

Section: Resultsmentioning

confidence: 99%

Extracting finite state representations from recurrent models of Industrial Cyber Physical Systems

Brusaferri¹,

Matteucci

Spinelli³

et al. 2020

2020 7th International Conference on Control, Decision and Information Technologies (CoDIT)

Self Cite

View full text Add to dashboard Cite

Section: Resultsmentioning

confidence: 99%

Extracting finite state representations from recurrent models of Industrial Cyber Physical Systems

Brusaferri¹,

Matteucci

Spinelli³

et al. 2020

2020 7th International Conference on Control, Decision and Information Technologies (CoDIT)

Self Cite

View full text Add to dashboard Cite

“…Particle swarm optimization (PSO) is a computational model for real continuous space, and it is difficult to solve the task scheduling problem in discrete space [23]. Therefore, the binary system is used to encode the speed and position of particles, and the mapping from the particle swarm optimization algorithm to discrete space, and from the particle search space to the optimal scheduling scheme, is realized by reconstructing the particle expression.…”

Section: The Coding Strategy For Optimal Scheduling Of Drppmentioning

confidence: 99%

An Optimal Scheduling Method for Data Resources of Production Process Based on Multicommunity Collaborative Search Algorithm

et al. 2022

View full text Add to dashboard Cite

Aiming at the problem of low response speed and unbalanced distribution of data resources of production process (DRPP) for the distributed workshop production environment, an optimization scheduling method of DRPP based on a multicommunity cooperative search algorithm is proposed. A heuristic data resource service scheduling framework including a load manager and dynamic scheduling engine is first built to deal with the uncertainty of data resource service response and the imbalance of resource allocation; a core scheduling optimization mathematical model with the objectives: resource service efficiency, reduced response time, and load balancing, is established. Then, a multicommunity cooperative search algorithm for the scheduling model is presented, and the mapping relationship between the particle position vector and resource allocation is established via binary coding. Thus, the optimization algorithm is mapped to discrete data space, and the multicommunity bidirectional driving evolutionary mechanism is used to realize the cooperative and interactive search between common and model community, which enhances the adaptability of the algorithm to dynamic random scheduling tasks. Finally, the effectiveness of the proposed method is verified by an example of multiprocess quality prediction service scheduling in silk production process, which provides an effective means for solving the complex scheduling problem of production process data.

show abstract

“…A pilot plant for mechatronic product demanufacturing, Figure 3, has been developed to support a more sustainable End-Of-Life treatment of printed circuit boards (PCB), from product re-qualification, to sub-parts reuse up to material recovery [46]. The process includes a robotized cell to de/re-assembly the board from/in the mechatronic product, a flying-probe based circuit test machine, a robotized PCB rework station and a material recovery cell including shredding and separation machines [47]. A conveyor system aimed to automatically transport the heterogeneous PCBs (mounted on pallets) through the operating stations.…”

Section: Flexible Conveyor Of the Pcb Remanufacturing Plantmentioning

confidence: 99%

Learning behavioral models by recurrent neural networks with discrete latent representations with application to a flexible industrial conveyor

Brusaferri

Matteucci

Spinelli

et al. 2020

Computers in Industry

View full text Add to dashboard Cite

Integrated automation system with PSO based scheduling for PCB remanufacturing plants

Cited by 4 publications

References 13 publications

Extracting finite state representations from recurrent models of Industrial Cyber Physical Systems

Extracting finite state representations from recurrent models of Industrial Cyber Physical Systems

An Optimal Scheduling Method for Data Resources of Production Process Based on Multicommunity Collaborative Search Algorithm

Learning behavioral models by recurrent neural networks with discrete latent representations with application to a flexible industrial conveyor

Contact Info

Product

Resources

About