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2020_makespanprediction_jinghuang

Huang, J., Chang, Q., & Arinez, J. (2020). Product Completion Time Prediction Using A Hybrid Approach Combining Deep Learning and System Model. Journal of Manufacturing Systems, 57, 311-322.

bffjsp

Bi-objective flexible job shop scheduling problems

chempropbayes

Bayesian MPNNs for Molecular Property Prediction

cpdea

Handling Imbalance Between Convergence and Diversity in the Decision Space in Evolutionary Multi-Modal Multi-Objective Optimization (IEEE Transactions on Evolutionary Computation)

dag-gnn

deepnest

An open source nesting application for laser cutters, plasma cutters and other CNC machines

den-armoea

# Introduction of DNN-AR-MOEA This repository contains code necessary to reproduce the experiments presented in Evolutionary Optimization of High-DimensionalMulti- and Many-Objective Expensive ProblemsAssisted by a Dropout Neural Network. Gaussian processes are widely used in surrogate-assisted evolutionary optimization of expensive problems. We propose a computationally efficient dropout neural network (EDN) to replace the Gaussian process and a new model management strategy to achieve a good balance between convergence and diversity for assisting evolutionary algorithms to solve high-dimensional multi- and many-objective expensive optimization problems. mainlydue to the ability to provide a confidence level of their outputs,making it possible to adopt principled surrogate managementmethods such as the acquisition function used in Bayesian opti-mization. Unfortunately, Gaussian processes become less practi-cal for high-dimensional multi- and many-objective optimizationas their computational complexity is cubic in the number oftraining samples. # References If you found DNN-AR-MOEA useful, we would be grateful if you cite the following reference: Evolutionary Optimization of High-DimensionalMulti- and Many-Objective Expensive ProblemsAssisted by a Dropout Neural Network (IEEE Transactions on Systems, Man and Cybernetics: Systems).

diferential_evolution

Hybrid differential evolution algorithm (HDE) to solve the Flexible Job Shop Scheduling Problem

drl_to_dfjsp

this repository is used to reappear thesis《Dynamic scheduling for flexible job shop with new job insertions by deep reinforcement learning》

flow-shop-scheduling-based-on-reinforcement-learning-algorithm

Operations Research Application Project - Flow Shop Scheduling Based On Reinforcement Learning Algorithm

genetic-algorithm-for-job-shop-scheduling-and-nsga-ii

Learning how to implement GA and NSGA-II for job shop scheduling problem in python

gnn_jssp

Master's Thesis - Graph Neural Networks for Compact Representation for Job Shop Scheduling Problems: A Comparative Benchmark

hdnnmv2.0

use a new DNN net work to solute the job shop problem, deep learning method named HDNNM

hello-world

this is test program

hybrid-flow-shop-scheduling

Multi-objective evolutionary algorithms integrated with different heuristic decoding methods for hybrid flow shop scheduling problem with worker constraint

job-shop-scheduling

This is a program to solve the job shop scheduling problem by using the parallel genetic algorithm

job-shop-scheduling-1

Job Shop Scheduling

jobshopscheduling

Job Shop Scheduling metaheuristics

jssp-solver

Job Shop Scheduling Problem Solver

lbd-moea

The source code of LBD-MOEA, i.e., "A Multi-objective Evolutionary Algorithm for Finding Knee Regions Using Two Localized Dominance Relationships"

libnest2d

2D irregular bin packaging and nesting library written in modern C++

menuplanning_moead_mathematics

Analisys of experimental results MA vs. ILS-MOEA/D

mpitutorial

MPI programming lessons in C and executable code examples

nest2d

Nest2D is a 2D bin packaging tool for python.

nesting

Graphical algorithm to find possible solutions to 2D nesting problems.

pymultiobjective

A python library for the following Multiobjective Objectives Optimization Algorithms or Many Objectives Optimization Algorithms: C-NSGA II; CTAEA; GrEA; IBEA; MOEA/D; NAEMO; NSGA II; NSGA III; OMOPSO; PAES; RVEA; SMPSO; SPEA2; U-NSGA III

pytorch-bayesiancnn

Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.

tyxe

use-modified-penetration-depth-and-guided-search-to-solve-nesting-problem