Production Planning Digital Twin for Complex Workflow Optimization
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Solution Overview
Problem
Current production planning and control methods struggle to efficiently optimize complex production workflows due to changing parameters and optimality criteria, leading to frequent re-planning needs and inefficiencies, especially in large-scale production environments with varying product complexity and constraints.
Innovation Solution
An adaptive computer-implemented method and system that simulates production processes as a digital twin, allowing for quick adaptation to changes and extended planning horizons, using evolutionary algorithms to optimize production sequences, worker assignments, and supplier orders based on a total cost function, ensuring efficient resource utilization and minimizing delays and stock levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional successive planning methods are used for production control, then the system is easier to implement and understand, but the optimization quality and ability to handle complex constraints deteriorates
Solution Approach 1:
The patent replaces traditional mechanical successive planning methods with a hybrid computational system combining evolutionary algorithms and local search techniques. This substitution enables handling of complex constraints and optimization criteria that were previously intractable, while the system provides user-friendly interfaces and visualization tools to maintain ease of use.
Solution Approach 2:
The patent segments the production planning problem into multiple independent modules: constraint modeling, objective function definition, evolutionary optimization, local search refinement, and solution validation. Each module can be configured and optimized independently, making the complex system manageable while achieving high optimization quality.
2Productivity
If evolutionary optimization algorithms are used, then the optimization quality improves, but the resource consumption (time and computing power) increases significantly
Solution Approach 1:
The patent performs preliminary actions by pre-defining constraint models, objective functions, and parameter ranges before the optimization run. The evolutionary algorithm starts with a warm initialization based on historical data and preliminary analysis, reducing the search space and avoiding redundant computations, thus achieving high optimization quality with reduced computing time.
Solution Approach 2:
The patent implements a periodic hybrid optimization approach where evolutionary algorithms perform coarse-grained exploration at lower frequency, followed by local search refinement at higher frequency. This periodic alternation between global and local optimization strategies maintains high optimization quality while significantly reducing overall computing time compared to continuous evolutionary search.
3Loss of time
If local search algorithms are used for optimization, then the computing time is reduced, but the ability to escape local optima and find global optima deteriorates
Solution Approach 1:
The patent introduces evolutionary algorithms as an intermediary between the problem definition and local search refinement. The evolutionary component generates diverse initial solutions and guides the local search toward promising regions of the search space, preventing premature convergence to local optima while maintaining the computational efficiency of local search methods.
4Measurement precision
If constraint-based linear programming approaches are used, then the planning precision improves, but the scalability to real problem sizes deteriorates
Solution Approach 1:
The patent implements a dynamic optimization framework where the level of constraint strictness and modeling detail can be adjusted based on problem size and available computational resources. The system dynamically selects between different optimization strategies (evolutionary vs. local search) and adjusts parameter granularity, maintaining planning precision for critical constraints while scaling to large production systems.
Data Source
AI summary
A computer-implemented method for planning and/or controlling a production by a production system comprising a plurality of production sections and production lines. A production planning and/or control system for production optimization is also disclosed.