Job Scheduling in Customized Assembly Lines Affected by Workers’ Learning

2011 ◽  
pp. 333-347
Author(s):  
Michel Anzanello ◽  
Flavio Fogliatto
Keyword(s):  
Job Scheduling ◽  
Assembly Lines

Cyclic job scheduling in paced assembly lines with cross-trained workers

2007 ◽  
Vol 45 (4) ◽  
pp. 803-828 ◽  
Author(s):  
J. K. Winch ◽  
X. Cai ◽  
G. L. Vairaktarakis
Keyword(s):  
Job Scheduling ◽  
Assembly Lines

Dependable grid job scheduling mechanism

2010 ◽  
Vol 30 (8) ◽  
pp. 2066-2069
Author(s):  
Yong-cai TAO ◽  
Lei SHI
Keyword(s):  
Job Scheduling ◽  
Grid Job Scheduling

Multi-level Parallelization of Genotype Imputation on Supercomputers

2020 ◽  
Vol 15 ◽  
Author(s):  
Weiwen Zhang ◽  
Long Wang ◽  
Theint Theint Aye ◽  
Juniarto Samsudin ◽  
Yongqing Zhu
Keyword(s):  
Association Study ◽  
Message Passing ◽  
High Performance ◽  
Message Passing Interface ◽  
Genome Wide Association Study ◽  
Job Scheduling ◽  
Genotype Imputation ◽  
Job Level ◽  
Multi Level ◽  
High Performance Requirement

Background: Genotype imputation as a service is developed to enable researchers to estimate genotypes on haplotyped data without performing whole genome sequencing. However, genotype imputation is computation intensive and thus it remains a challenge to satisfy the high performance requirement of genome wide association study (GWAS). Objective: In this paper, we propose a high performance computing solution for genotype imputation on supercomputers to enhance its execution performance. Method: We design and implement a multi-level parallelization that includes job level, process level and thread level parallelization, enabled by job scheduling management, message passing interface (MPI) and OpenMP, respectively. It involves job distribution, chunk partition and execution, parallelized iteration for imputation and data concatenation. Due to the design of multi-level parallelization, we can exploit the multi-machine/multi-core architecture to improve the performance of genotype imputation. Results: Experiment results show that our proposed method can outperform the Hadoop-based implementation of genotype imputation. Moreover, we conduct the experiments on supercomputers to evaluate the performance of the proposed method. The evaluation shows that it can significantly shorten the execution time, thus improving the performance for genotype imputation. Conclusion: The proposed multi-level parallelization, when deployed as an imputation as a service, will facilitate bioinformatics researchers in Singapore to conduct genotype imputation and enhance the association study.

Race, Work, and Disability in Progressive Era United States

2018 ◽  
Author(s):  
Paul Lawrie
Keyword(s):  
World War I ◽  
Progressive Era ◽  
Turn Of The Century ◽  
Assembly Lines ◽  
Actuarial Science ◽  
World War ◽  
Antebellum Era ◽  
Black Bodies ◽  
Mental Testing ◽  
Twentieth Century America

Throughout U.S. history, the production of difference, whether along racial or disability lines, has been inextricably tied to the imperatives of labor economy. From the plantations of the antebellum era through the assembly lines and trenches of early-twentieth-century America, ideologies of race and disability have delineated which peoples could do which kinds of work. The ideologies and identities of race, work, and the “fit” ’ or “unfit” body informed Progressive Era labor economies. Here the processes of racializing or disabling certain bodies are charted from turn-of-the-century actuarial science, which monetized blacks as a degenerate, dying race, through the standardized physical and mental testing and rehabilitation methods developed by the U.S. army during World War I. Efforts to quantify, poke, prod, or mend black bodies reshaped contemporary understandings of labor, race, the state, and the working body.

scholarly journals Transformation of traditional assembly lines into interoperable CPPS for MES: an OPC UA enabled scenario

2021 ◽  
Vol 54 ◽  
pp. 118-123
Author(s):  
N. Szántó ◽  
G. Pedone ◽  
G. Monek ◽  
B. Háy ◽  
J. Jósvai
Keyword(s):  
Assembly Lines

scholarly journals Digital Twin for Designing and Reconfiguring Human–Robot Collaborative Assembly Lines

2021 ◽  
Vol 11 (10) ◽  
pp. 4620
Author(s):  
Niki Kousi ◽  
Christos Gkournelos ◽  
Sotiris Aivaliotis ◽  
Konstantinos Lotsaris ◽  
Angelos Christos Bavelos ◽  
...  
Keyword(s):  
Production System ◽  
Assembly Lines ◽  
Flexible Assembly ◽  
Digital Twin ◽  
Collaborative Assembly ◽  
Geometrical Information ◽  
Line Level ◽  
Dual Arm ◽  
Different Levels

This paper discusses a digital twin-based approach for designing and redesigning flexible assembly systems. The digital twin allows modeling the parameters of the production system at different levels including assembly process, production station, and line level. The approach allows dynamically updating the digital twin in runtime, synthesizing data from multiple 2D–3D sensors in order to have up-to-date information about the actual production process. The model integrates both geometrical information and semantics. The model is used in combination with an artificial intelligence logic in order to derive alternative configurations of the production system. The overall approach is discussed with the help of a case study coming from the automotive industry. The case study introduces a production system integrating humans and autonomous mobile dual arm workers.

scholarly journals Power and Performance Evaluation of Memory-Intensive Applications

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4089
Author(s):  
Kaiqiang Zhang ◽  
Dongyang Ou ◽  
Congfeng Jiang ◽  
Yeliang Qiu ◽  
Longchuan Yan
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Power Consumption ◽  
Job Scheduling ◽  
Memory System ◽  
Processor Core ◽  
Memory Efficiency ◽  
And Performance ◽  
Reasonable Use ◽  
Server System

In terms of power and energy consumption, DRAMs play a key role in a modern server system as well as processors. Although power-aware scheduling is based on the proportion of energy between DRAM and other components, when running memory-intensive applications, the energy consumption of the whole server system will be significantly affected by the non-energy proportion of DRAM. Furthermore, modern servers usually use NUMA architecture to replace the original SMP architecture to increase its memory bandwidth. It is of great significance to study the energy efficiency of these two different memory architectures. Therefore, in order to explore the power consumption characteristics of servers under memory-intensive workload, this paper evaluates the power consumption and performance of memory-intensive applications in different generations of real rack servers. Through analysis, we find that: (1) Workload intensity and concurrent execution threads affects server power consumption, but a fully utilized memory system may not necessarily bring good energy efficiency indicators. (2) Even if the memory system is not fully utilized, the memory capacity of each processor core has a significant impact on application performance and server power consumption. (3) When running memory-intensive applications, memory utilization is not always a good indicator of server power consumption. (4) The reasonable use of the NUMA architecture will improve the memory energy efficiency significantly. The experimental results show that reasonable use of NUMA architecture can improve memory efficiency by 16% compared with SMP architecture, while unreasonable use of NUMA architecture reduces memory efficiency by 13%. The findings we present in this paper provide useful insights and guidance for system designers and data center operators to help them in energy-efficiency-aware job scheduling and energy conservation.

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