Optimizing Manufacturing Scheduling with Genetic Algorithm and LSTM Neural Networks
Hongyue Sun
Abstract
In response to Industry 4.0 and the rise of intelligent manufacturing, this study develops a system combining Long Short-Term Memory (LSTM) Neural Networks and a Multi-Objective Genetic Algorithm to improve prediction and optimization in manufacturing scheduling.A novel model predicts work-in-process (WIP) inventory using LSTM neural networks, accommodating dynamic changes in production.A manufacturing scheduling model is also created and solved using a multi-objective genetic algorithm, simplifying the resolution process and obtaining practical solutions.These methods provide a valuable approach to optimizing production scheduling in intelligent manufacturing, enhancing efficiency and economic gains.
Topics & Concepts
Computer scienceArtificial neural networkGenetic algorithmScheduling (production processes)Artificial intelligenceAlgorithmMachine learningMathematical optimizationMathematicsManufacturing Process and OptimizationScheduling and Optimization AlgorithmsAdvanced Manufacturing and Logistics Optimization