How can machine learning improve production planning and scheduling?

Em algumas ocasiões no trabalho, o time de engenharia fez alguns estudos para entender quais os itens dentro de um processo produtivo petroquímicos não estavam otimizados. Escolhemos utilizar a ferramenta de Machine learning para entender às variações de entrada dos processos e criar modelos que tinham propósito de controlar o sistema de automação para otimizar o processo produtivo. Com o processo industrial otimizado, reduziu-se o grau de incerteza de trabalho, reduzindo desvios da normalidade, o planejamento da oferta dos produtos produziu atingiu alta previsibilidade, o que tornou mais fácil e eficaz o processo de venda.

Machine learning can improve production planning and scheduling by analyzing historical production data, identifying patterns, and predicting future demand. It helps optimize production schedules by considering various factors such as equipment availability, workforce capacity, and supply chain constraints. Machine learning algorithms adapt to changing conditions, providing real-time insights for efficient decision-making. This technology enhances accuracy in forecasting, resource allocation, and overall production planning, contributing to increased efficiency and cost-effectiveness.

Frequent reconfiguration of production processes increases the complexity of production planning and control (PPC). Software-defined manufacturing offers a solution by automating and enhancing PPC through service-oriented architecture. Here’s how: Automation and Interoperability: By treating PPC tasks as services, we encapsulate and reuse these tasks, reducing manual intervention. Service-Oriented Architecture: This architecture, based on asset administration shells, connects production data sources seamlessly, allowing planners to manage PPC tasks without worrying about implementation details. OpenAPI Integration: Mapping AAS to OpenAPI Specifications ensures interoperable and semantic integration of existing services and legacy systems.

Machine learning can greatly improve production planning and scheduling by offering models for different challenges. Time series forecasting predicts demand and supply based on historical data. Reinforcement learning optimizes real-time decisions, like machine scheduling or shift changes. Constraint satisfaction models help balance multiple constraints, such as machine capacity and deadlines. Decision trees and clustering group similar tasks or resources for efficiency. Neural networks identify complex patterns for predicting issues like machine failures. Bayesian optimization fine-tunes production parameters. Together, these models create a more adaptive, efficient production system.

The future of quality assurance is shifting towards function-oriented measurements. Traditionally, standardized parameters were the norm, but now, functional models and simulations created during the Functional Models as Virtual Gauges: These models enable individual analysis of multiple measurement results, acting as “virtual functional gauges.” High Model Quality: Combining high-quality functional models with low measurement uncertainty reduces functional uncertainty, making functional capability predictable. Application areas exist in: - geometrical deviations / tolerances of diesel injectors - functions of microstructed surfaces - load-handling capabilites of fiber reinforced polymers

Em algumas ocasiões no trabalho, o time de engenharia fez alguns estudos para entender quais os itens dentro de um processo produtivo petroquímicos não estavam otimizados. Escolhemos utilizar a ferramenta de Machine learning para entender às variações de entrada dos processos e criar modelos que tinham propósito de controlar o sistema de automação para otimizar o processo produtivo. Com o processo industrial otimizado, reduziu-se o grau de incerteza de trabalho, reduzindo desvios da normalidade, a qualidade do produto se tornou mais constante e otimizada aos limites de especificação, o que tornou mais confiável o processo de venda.

Maintenance scheduling is a critical balance between too early and too late actions. Early maintenance can lead to high costs and lower equipment utilization. Delayed maintenance, on the other hand, results in frequent breakdowns and lost productivity. A key factor in this balance is equipment utilization. Maintenance during high utilization impacts throughput significantly, whereas during low utilization, the impact is minimal. To address this, an adaptive reinforcement learning (RL) algorithm can be integrated into a decision-making support system. This system continuously monitors equipment failure and utilization distributions, proposing optimal maintenance times. The case has been proven in semiconductor manufacturing.