Now showing items 1-10 of 124
End-to-end on-line rescheduling from Gantt chart images using deep reinforcement learning
(Taylor & Francis Ltd, 2021-11-26)
With the advent of the socio-technical manufacturing paradigm, the way in which reschedulingdecisions are taken at the shop floor has radically changed in order to guarantee highly efficient production under increasingly ...
Reinforcement Learning using Gaussian Processes for Discretely Controlled Continuous Processes
(Planta Piloto de Ingeniería Química, 2013-07)
In many application domains such as autonomous avionics, power electronics and process systems engineering there exist discretely controlled continuous processes (DCCPs) which constitute a special subclass of hybrid dynamical ...
Learning budget assignment policies for autoscaling scientific workflows in the cloud
Autoscalers exploit cloud-computing elasticity to cope with the dynamic computational demands of scientific workflows. Autoscalers constantly acquire or terminate virtual machines (VMs) on-the-fly to execute workflows ...
Learning and adaptation of a policy for dynamic order acceptance in make-to-order manufacturing
(Pergamon-Elsevier Science Ltd, 2010)
An adaptive deep reinforcement learning approach for MIMO PID control of mobile robots
(Elsevier Science Inc., 2020-02)
Intelligent control systems are being developed for the control of plants with complex dynamics. However, the simplicity of the PID (proportional–integrative–derivative) controller makes it still widely used in industrial ...
Collaborative Learning: A Model of Strategies to Apply in University Teaching
(Center for Promoting Ideas, 2017-06)
Collaborative learning is a construct that identifies a strong field nowadays, both traditional on-site and virtual education. The article aims to present a model of strategies that teachers can implement to develop ...
Public policy modeling and applications
(John Wiley & Sons Inc, 2019-01)
Two expected results of public policies are fostering economic development and show adaptive capacity in case of disruptions. e latter is linked to the concept of resilience. In the research paper by G. Castañeda and O. ...
Real-time rescheduling of production systems using relational reinforcement learning
(QUALIS CAPES (UFSC), 2011-12)
Most scheduling methodologies developed until now have laid down good theoretical foundations, but there is still the need for real-time rescheduling methods that can work effectively in disruption management. In this ...
Learning processes as drivers of change in industrial foresto SMEs Misiones
(Universidad Nacional de Misiones. Facultad de Ciencias Económicas. Programa de Posgrado en Administración, 2014-12-19)
This is to analyze the behavior and actions of agents related to those changes produced in the emergence and spread of the driving forces of the change of SME enterprises in the Foresty – industry sector and related ...
Towards Autonomous Reinforcement Learning: Automatic Setting of Hyper-parameters using Bayesian Optimization
(CLEI (Latin-american Center for Informatics Studies), 2018)
With the increase of machine learning usage by industries and scientific communities in a variety of tasks such as text mining, image recognition and self-driving cars, automatic setting of hyper-parameter in learning ...