Machine learning-based dynamic production planning and control in unreliable manufacturing systems with supply disruptions

Keyphrases

• Control Policy 100%
• Machine Learning Based 100%
• Unreliable Manufacturing System 100%
• Delivery Lead Time 100%
• Supply Disruption 100%
• Dynamic Production Planning 100%
• Dynamic Production Control 100%
• Replenishment 66%
• Integrated Production 33%
• Production Rate 33%
• Machine Learning Models 33%
• Economic Performance 33%
• Production Planning Problem 33%
• Simulation Modeling 33%
• Experimental Simulation 33%
• Production Control 33%
• Hedging Point 33%
• Policy Parameters 33%
• Production Policy 33%
• Cost Reduction 33%
• Machine Learning Techniques 33%
• Time Variation 33%
• System State 33%
• Supply Uncertainty 33%
• Raw Material Supply 33%
• Failure-prone Manufacturing System 33%
• Control Problem 33%
• Dynamic Control 33%
• Production Environment 33%
• Dynamically Adjust 33%
• Machine Learning-based Approach 33%
• Replenishment Strategy 33%
• Numerical Experiments 33%
• Time Variability 33%
• Dynamic Policy 33%
• System Status 33%
• Design Modeling 33%
• Economic Order Quantity 33%
• Resilient Performance 33%
• Semi-dynamic 33%
• Material Delivery 33%
• Stochastic Conditions 33%
• Static Policy 33%
• Systems Delivery 33%
• Continuous Adjustment 33%
• Order Size 33%

Engineering

• Learning System 100%
• Production Planning 100%
• Policy Control 100%
• Gas Fuel Manufacture 100%
• Disruption 100%
• Lead Time 100%
• Design of Experiments 33%
• System State 33%
• Production Environment 33%
• Numerical Experiment 33%
• Machine Learning Technique 33%
• Dynamic Policy 33%
• Replenishment Order 33%
• Control Dynamics 33%
• Experimental Design 33%

Chemical Engineering

• Learning System 100%