Short-term manpower management in manufacturing systems: new requirements and DSS prototyping

The short-term planning and scheduling of discrete manufacturing systems has mostly focused in the past on the management of machines, implicitly considered as the critical resources of the workshops. Some of the present schedulers claim to also manage human resources, but perform most of the time a local allocation of operators to machines, these operators having regular working hours. However, it seems clear that the workforce has a specificity that should be better taken into account by short-term planning facilities. Moreover, the variability of the weekly working hours through the year will shortly become a rule and not anymore an exception. On the base of a questionnaire answered by 19 French companies of different sizes and industrial sectors, we have tried to identify more precisely some industrial requirements concerning the short-term management of human resources. The growing interest in annualised hours together with the lack of software tools that allow to implement it practically is one of the results of this questionnaire. We suggest in this article the specification of a decision support system for short-term manpower management under annualised hours, taking into account the competence of the operators. A software prototype has been developed according to these specifications; the results of a simple but representative example are described

Runtime-guided mitigation of manufacturing variability in power-constrained multi-socket NUMA nodes

This work has been supported by the Spanish Government (Severo Ochoa grants SEV2015-0493, SEV-2011-00067), by the Spanish Ministry of Science and Innovation (contracts TIN2015-65316-P), by Generalitat de Catalunya (contracts 2014-SGR-1051 and 2014-SGR-1272), by the RoMoL ERC Advanced Grant (GA 321253) and the European HiPEAC Network of Excellence. M. Moretó has been partially supported by the Ministry of Economy and Competitiveness under Juan de la Cierva postdoctoral fellowship number JCI-2012-15047. M. Casas is supported by the Secretary for Universities and Research of the Ministry of Economy and Knowledge of the Government of Catalonia and the Cofund programme of the Marie Curie Actions of the 7th R&D Framework Programme of the European Union (Contract 2013 BP B 00243). This work was also partially performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 (LLNL-CONF-689878). Finally, the authors are grateful to the reviewers for their valuable comments, to the RoMoL team, to Xavier Teruel and Kallia Chronaki from the Programming Models group of BSC and the Computation Department of LLNL for their technical support and useful feedback.Peer ReviewedPostprint (published version

Learning Scheduling Algorithms for Data Processing Clusters

Efficiently scheduling data processing jobs on distributed compute clusters requires complex algorithms. Current systems, however, use simple generalized heuristics and ignore workload characteristics, since developing and tuning a scheduling policy for each workload is infeasible. In this paper, we show that modern machine learning techniques can generate highly-efficient policies automatically. Decima uses reinforcement learning (RL) and neural networks to learn workload-specific scheduling algorithms without any human instruction beyond a high-level objective such as minimizing average job completion time. Off-the-shelf RL techniques, however, cannot handle the complexity and scale of the scheduling problem. To build Decima, we had to develop new representations for jobs' dependency graphs, design scalable RL models, and invent RL training methods for dealing with continuous stochastic job arrivals. Our prototype integration with Spark on a 25-node cluster shows that Decima improves the average job completion time over hand-tuned scheduling heuristics by at least 21%, achieving up to 2x improvement during periods of high cluster load