Closed-loop Rescheduling using Deep Reinforcement Learning

In this work, a novel approach for generating rescheduling knowledge which can be used in real-time for handling unforeseen events without extra deliberation is presented. For generating such control knowledge, the rescheduling task is modelled and solved as a closed-loop control problem by resortin...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Palombarini, Jorge A., Martínez, Ernesto C.
Formato:	Objeto de conferencia Resumen
Lenguaje:	Inglés
Publicado:	2019
Materias:	Ciencias Informáticas Control knowledge Schedule state simulator Computational tool
Acceso en línea:	http://sedici.unlp.edu.ar/handle/10915/89513
Aporte de:	SEDICI (UNLP) de Universidad Nacional de La Plata

Ejemplares similares

Real-time Rescheduling of Production Systems using Relational Reinforcement Learning
por: Palombarini, Jorge, et al.
Publicado: (2011)

Generating rescheduling knowledge using reinforcement learning in a cognitive architecture
por: Palombarini, Jorge, et al.
Publicado: (2014)

Aerospace Manufacturing Industry: A Simulation-based Decision Support Framework for the Scheduling of Complex Hoist Lines
por: Basán, Natalia P., et al.
Publicado: (2015)

Automated Task Rescheduling using Relational Markov Decision Processes with Logical State Abstractions
por: Palombarini, Jorge, et al.
Publicado: (2012)

An agent-based simulation model using decoupled learning rules to (re)schedule multiple projects
por: Tosselli, Laura, et al.
Publicado: (2017)

A variant of simulated annealing to solve unrestricted identical parallel machine scheduling problems
por: Gatica, Claudia Ruth, et al.
Publicado: (2013)

Knowledge Insertion: an Efficient Approach to Reduce Search Effort in Evolutionary Scheduling
por: Pandolfi, Daniel, et al.
Publicado: (2004)

CISNE-P: a global scheduling oriented to now environments
por: Torchinsky, Matías, et al.
Publicado: (2007)

A cognitive approach to real-time rescheduling using SOAR-RL
por: Barsce, Juan Cruz, et al.
Publicado: (2013)

Autoscaling scientific workflows on the cloud by combining on-demand and spot instances
por: Monge, D.A., et al.

Autoscaling scientific workflows on the cloud by combining on-demand and spot instances
Publicado: (2017)

El planificador de procesos a través de un simulador
por: Barrionuevo, Mercedes, et al.
Publicado: (2009)

The role of different crossover methods when solving the open shop scheduling problem via a simple evolutionary approach
por: Beraudo, Vanina, et al.
Publicado: (2002)

Using Cell-DEVS for modeling complex cell spaces
por: Ameghino, J., et al.

Using Cell-DEVS for modeling complex cell spaces
Publicado: (2005)

Inserting knowledge in multirecombined evolutionary algorithms for the flow shop scheduling problem
por: Villagra, Andrea, et al.
Publicado: (2002)

Knowledge insertion: an efficient approach to reduce search effort in evolutionary scheduling
por: Pandolfi, Daniel, et al.
Publicado: (2003)

GPU implementations of scheduling heuristics for heterogeneous computing environments
por: Nesmachnow, Sergio, et al.
Publicado: (2011)

Metaheuristics for production scheduling
Publicado: (2013)

A genetic approach using direct representation of solution for the parallel task scheduling problem
por: Esquivel, Susana Cecilia, et al.
Publicado: (2001)

Upgrading evolutionary algorithms through multiplicity for multiobjective optimization in job shop scheduling problems
por: Esquivel, Susana Cecilia, et al.
Publicado: (2001)

Multiple crossovers on multiple parents for the multiobjective flow shop problem
por: Esquivel, Susana Cecilia, et al.
Publicado: (2002)

Defy the Game: Automated Market Making using Deep Reinforcement Learning
por: Parrotta, Agustín
Publicado: (2023)

Application of virtual reality in a learning experience
por: Obrist Bertrand, Víctor Udo, et al.
Publicado: (2015)

Designing a Multi-Period Water Distribution Network with a Hybrid Simulated Annealing
por: Bermúdez, Carlos, et al.
Publicado: (2019)

Including accurate user estimates in HPC schedulers: ban empirical analysis
por: Rocchetti, Néstor, et al.
Publicado: (2015)

A Tool for GIXRF/XRR simulation and data analysis
por: Vakula, N. I., et al.
Publicado: (2021)

A Tool for GIXRF/XRR simulation and data analysis
por: Vakula, N. I., et al.
Publicado: (2021)

Multiobjetive optimization co-evolution for the job shop scheduling problem
por: Esquivel, Susana Cecilia, et al.
Publicado: (1999)

A tutorial on the implementations of linear image filters in CPU and GPU
por: Pardo, Álvaro
Publicado: (2017)

Verification of real-time designs: Combining scheduling theory with automatic formal verification
por: Braberman, V.A., et al.

Verification of real-time designs: Combining scheduling theory with automatic formal verification
por: Braberman, Víctor Adrián
Publicado: (1999)

Application of virtual reality in a learning experience
por: Obrist Bertrand, Víctor Udo, et al.
Publicado: (2015)

Multirecombinated evolutionary algorithms to solve multiobjective job shop scheduling
por: Esquivel, Susana Cecilia, et al.
Publicado: (2000)

Una propuesta de una herramienta flexible para problemas de scheduling
por: Leguizamón, Guillermo, et al.
Publicado: (2004)

Applied deep learning and computer vision for self-driving cars build autonomous vehicles using deep neural networks and behavior-cloning techniques /
por: Ranjan, Sumit
Publicado: (2020)

Image segmentation using morphological tools
por: Pardo, Álvaro
Publicado: (2002)

Integrating interactive tools using concurrent haskell and synchronous events
por: Karlsen, Einar W.
Publicado: (1997)

Using combination of actions in reinforcement learning
por: Karanik, Marcelo J., et al.
Publicado: (2010)

The use of the 'Materiality Argument' in the literature on computer simulations
por: Durán, Juan Manuel, et al.
Publicado: (2021)