Deep reinforcement learning for dynamic scheduling of a flexible job shop
The ability to handle unpredictable dynamic events is becoming more important in pursuing
agile and flexible production scheduling. At the same time, the cyber-physical convergence …
agile and flexible production scheduling. At the same time, the cyber-physical convergence …
Synthesis, stability, thermophysical properties and AI approach for predictive modelling of Fe3O4 coated MWCNT hybrid nanofluids
Stability and thermophysical properties of water-based magnetite (Fe 3 O 4) material coated
on multiwalled carbon nanotubes hybrid nanofluids was investigated. The in-situ growth …
on multiwalled carbon nanotubes hybrid nanofluids was investigated. The in-situ growth …
Comparative study using RSM and ANN modelling for performance-emission prediction of CI engine fuelled with bio-diesohol blends: A fuzzy optimization approach
This present investigation focuses on prediction of engine responses of a single cylinder CI
engine powered by bio-diesohol (diesel-palm biodiesel-ethanol) blends using RSM …
engine powered by bio-diesohol (diesel-palm biodiesel-ethanol) blends using RSM …
A hybrid ANN-Fuzzy approach for optimization of engine operating parameters of a CI engine fueled with diesel-palm biodiesel-ethanol blend
This paper investigates use of artificial neural network (ANN) model in prediction of brake
specific energy consumption (BSEC), nitrogen oxides (NO x), unburnt hydrocarbon (UHC) …
specific energy consumption (BSEC), nitrogen oxides (NO x), unburnt hydrocarbon (UHC) …
A deep multi-agent reinforcement learning approach to solve dynamic job shop scheduling problem
Manufacturing industry is experiencing a revolution in the creation and utilization of data, the
abundance of industrial data creates a need for data-driven techniques to implement real …
abundance of industrial data creates a need for data-driven techniques to implement real …
[HTML][HTML] Solving flow-shop scheduling problem with a reinforcement learning algorithm that generalizes the value function with neural network
J Ren, C Ye, F Yang - Alexandria engineering journal, 2021 - Elsevier
This paper solves the flow-shop scheduling problem (FSP) through the reinforcement
learning (RL), which approximates the value function with neural network (NN). Under the …
learning (RL), which approximates the value function with neural network (NN). Under the …
The marriage of operations research and reinforcement learning: Integration of NEH into Q-learning algorithm for the permutation flowshop scheduling problem
The permutation flowshop scheduling problem (PFSP) attracted much interest from the
operations research (OR) community, resulting in various heuristic and metaheuristic …
operations research (OR) community, resulting in various heuristic and metaheuristic …
Flashover voltage of porcelain insulator under various pollution distributions: Experiment and modeling
The current work investigates the flashover voltage of an 11 kV porcelain insulator against
changes in the location of the dry band, salt deposit density, humidity, non-uniform pollution …
changes in the location of the dry band, salt deposit density, humidity, non-uniform pollution …
A review of dynamic scheduling: context, techniques and prospects
Quite a lot of literature exists for static scheduling for shop-floor; static schedules, however,
become obsolete almost immediately as the systems experience unpredictable disruptions …
become obsolete almost immediately as the systems experience unpredictable disruptions …
An experimental study on enhancing performance and reducing emissions of CRDI engine operated on Co-pyrolysis oil using particle swarm optimization
This study explores the use of Artificial Neural Network and Particle Swarm Optimization to
improve the performance and emissions of a CRDI engine fuelled by a mixture of co …
improve the performance and emissions of a CRDI engine fuelled by a mixture of co …