Intralogistics Digital Twin Optimization
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Solution Overview
Problem
Intralogistics systems face challenges in optimizing operating conditions due to their complexity, with numerous components and interdependencies, making it difficult for operators to adapt and improve performance, especially under changing conditions such as sudden order peaks.
Innovation Solution
A method and system that utilize a simulation module to generate process data based on component configuration parameters, properties, and interaction properties, and a determination module to analyze and optimize these parameters to improve system performance, throughput, and efficiency, using machine learning and data mining tools to derive optimal settings for hardware and software components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the intralogistics system comprises a vast variety of different components to handle complex logistics flows, then the system's functionality and versatility are improved, but the device complexity and difficulty of operation increase
Solution Approach 1:
The patent creates a digital twin (virtual copy) of the intralogistics system that replicates all components, interactions, and operating conditions. This digital model allows operators to analyze, simulate, and optimize system behavior without physically manipulating the complex real system, thereby resolving the contradiction between high functionality and operational complexity.
2Ease of operation
If manual operation and adjustment of component settings is used, then the ease of operation is maintained, but the productivity and performance optimization capability deteriorate
Solution Approach 1:
The determination module automatically analyzes process data from the digital twin and self-determines optimized component configuration parameters without requiring manual operator intervention. The system performs self-optimization by evaluating performance metrics and autonomously adjusting settings, thereby maintaining ease of operation while significantly improving productivity and performance.
3Adaptability or versatility
If the system operates under changing external conditions such as order peaks, then the adaptability is improved, but the reliability and performance stability worsen
Solution Approach 1:
The system uses the digital twin to perform preliminary simulations and analysis under various external conditions (e.g., order peaks, Black Friday scenarios) before they occur in the real system. By pre-determining optimized parameters for different scenarios, the system maintains performance stability and reliability when actual changes occur, as the optimized settings are already prepared and can be quickly applied.
4Measurement precision
If comprehensive process data collection from all components is implemented, then the measurement precision and analysis capability are improved, but the device complexity and data processing requirements increase
Solution Approach 1:
The digital twin serves as an intermediary between the complex real system and the analysis/optimization processes. It collects, structures, and processes comprehensive process data from all components in a virtual environment, transforming raw data into meaningful insights without requiring direct complex processing of the real system's data streams. This intermediary layer simplifies data handling while maintaining high measurement precision.
Data Source
AI summary
A method for determining operating conditions of an intralogistics system with components like conveyors, storage systems, shuttles, and others includes a simulating step and a step of generating process data from that simulation. A determination module is configured to perform a step of determining modified component parameters and properties by analyzing process data while optimizing a predefined target variable of the intralogistics system. In another aspect, an intralogistics system has a simulation module and a determination module, both accordingly configured to analyze process data and determine optimized operating conditions of an intralogistics system.