AstroGeoThe statistical framework of AstroGeoFit. 4. Computational methods. By Nathan Hara, LAM
PeerTube
The statistical framework of AstroGeoFit. 3. Selecting the model complexity. By Nathan Hara, LAM
The statistical framework of AstroGeoFit. 3. Selecting the model complexity. By Nathan Hara, LAM
Jenetics GAOnline dynamic scheduling for multi-compartment AGV and cobot in manufacturing systems via remaining processing time integration.
https://doi.org/10.1007/s00170-026-18281-5
#Jenetics #GeneticAlgorithm #EvolutionaryAlgorithm #SmartManufacturing #DigitalTwin #OnlineScheduling #MultiCompartmentAGV #CollaborativeRobot
https://doi.org/10.1007/s00170-026-18281-5
#Jenetics #GeneticAlgorithm #EvolutionaryAlgorithm #SmartManufacturing #DigitalTwin #OnlineScheduling #MultiCompartmentAGV #CollaborativeRobot
Online dynamic scheduling for multi-compartment AGV and cobot in manufacturing systems via remaining processing time integration - The International Journal of Advanced Manufacturing Technology
This paper presents an online scheduling framework for a multi-compartment Automated Guided Vehicle (AGV) with optional onboard cobot, embedded in a Digital Twin environment that provides real-time machine-state data. The scheduler is a Genetic Algorithm (GA) with mission-index encoding and feasibility-by-construction initialization, generating valid sequences under capacity, traceability, and precedence constraints. To isolate the value of machine-state awareness, the GA operates in two modes: an RPT-blind baseline using logistics data only, and an RPT-aware extension that integrates machine Remaining Processing Time (RPT) into feasibility and fitness evaluation. Validation across eleven scenarios on a precision aerospace workshop reveals that the impact of RPT-awareness is regime-dependent. In non-saturated and capacity-constrained settings, readiness-driven synchronization reduces transport waste (Trip Time −34–51%, Empty Trip Time −54–70% vs. RPT-blind) but increases makespan by 5–11%, because conservative waiting can delay feeding of bottleneck resources. In coordination-sensitive and high-mix regimes, RPT-awareness improves both dimensions simultaneously, reducing makespan by up to 10% (810 $$\rightarrow$$ 728 min) alongside comparable logistics gains. Relative to the FIFO baseline representing current industrial practice, GA-based automation reduces Trip Time by 15–60% and Empty Trip Time by 15–77%. RPT-awareness also enables proactive cobot scheduling, completing all eligible inline missions (up to 29 per shift versus 4–9 in RPT-blind mode). The results indicate that RPT-aware scheduling is most effective where coordination complexity, rather than processing capacity, bounds system performance.
The statistical framework of AstroGeoFit. 1. The AstroGeoFit model. By Nathan Hara, LAM.
The statistical framework of AstroGeoFit. 1. The AstroGeoFit model. By Nathan Hara, LAM.
Patrick 
One Open-source Project Daily
🦄 An Artificial Inteligence to teach Google's Dinosaur to jump cactus
https://github.com/ivanseidel/IAMDinosaur
#1ospd #opensource #artificialintelligence #dino #geneticalgorithm #genome #googledinosaur #neuralnetwork
🦄 An Artificial Inteligence to teach Google's Dinosaur to jump cactus
https://github.com/ivanseidel/IAMDinosaur
#1ospd #opensource #artificialintelligence #dino #geneticalgorithm #genome #googledinosaur #neuralnetwork
The Promise of Static Profiling: Exploring the Limits.
https://doi.org/10.1145/3801119.3801123
#Jenetics #GeneticAlgorithm #StaticProfilers #DynamicProfilers #Profilers #OptimizingCompilers #MachineLearning #GraalVM #Java #NativeImage
https://doi.org/10.1145/3801119.3801123
#Jenetics #GeneticAlgorithm #StaticProfilers #DynamicProfilers #Profilers #OptimizingCompilers #MachineLearning #GraalVM #Java #NativeImage
GIANT: General intelligent AgeNt trainer.
https://doi.org/10.1016/j.softx.2026.102607
#Jenetics #MachineLearning #EvolutionaryAlgorithms #GeneticProgramming #GeneticAlgorithm #Optimization #Games #MultiAgentSystems
https://doi.org/10.1016/j.softx.2026.102607
#Jenetics #MachineLearning #EvolutionaryAlgorithms #GeneticProgramming #GeneticAlgorithm #Optimization #Games #MultiAgentSystems
GraalMHC: ML-Based Method-Hotness Classification for Binary-Size Reduction in Optimizing Compilers
#Jenetics #GeneticAlgorithm #StaticProfilers #OptimizingCompilers #MachineLearning #BinarySizeReduction #GraalVM #NativeImage
https://dl.acm.org/doi/10.1145/3771775.3786276
#Jenetics #GeneticAlgorithm #StaticProfilers #OptimizingCompilers #MachineLearning #BinarySizeReduction #GraalVM #NativeImage
https://dl.acm.org/doi/10.1145/3771775.3786276
Just released version 9.0.0 of the #Jenetics #Java library. It now requires #Java25 to compile and run.
#GeneticAlgorithm #EvolutionaryAlgorithm #GrammaticalEvolution #GeneticProgramming #MultiobjectiveOptimization #MOEA
https://github.com/jenetics/jenetics/releases/tag/v9.0.0
#GeneticAlgorithm #EvolutionaryAlgorithm #GrammaticalEvolution #GeneticProgramming #MultiobjectiveOptimization #MOEA
https://github.com/jenetics/jenetics/releases/tag/v9.0.0
Seasonal Reconfiguration of Electrical Distribution Systems to Mitigate the Impact of Electric Vehicle Charging,
https://ieeexplore.ieee.org/document/11298634
#Jenetics #GeneticAlgorithm #DistributedNetworks #Optimization #PowerLosses #EnergyLoss #VehicleToGrid #ElectricVehicle
https://ieeexplore.ieee.org/document/11298634
#Jenetics #GeneticAlgorithm #DistributedNetworks #Optimization #PowerLosses #EnergyLoss #VehicleToGrid #ElectricVehicle