Production Line Task Sequencing Under Changing Constraints
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Solution Overview
Problem
Existing methods for generating production line plans are inefficient due to unoptimized cycles caused by varying constraints over time, leading to increased processing time and costs, especially when managing multiple products on a single line.
Innovation Solution
A method using a genetic algorithm to generate an optimized tasks sequence by assigning weight coefficients to criteria, calculating penalty matrices, and applying a genetic algorithm to derive the sequence with the lowest sum of penalties, considering multiple criteria simultaneously.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of stationary object
If a long-term plan-to-produce cycle is used, then production planning coverage is improved, but constraints vary over time making the plan unoptimized and increasing processing time
Solution Approach 1:
The patent divides the long-term plan-to-produce cycle into multiple short-term optimization windows. Instead of optimizing the entire long-term cycle at once, the system segments it into manageable time periods, each optimized independently with current constraints, thereby maintaining optimality without excessive computational time.
Solution Approach 2:
The system dynamically adjusts the optimization horizon by implementing a rolling optimization approach. As time progresses and constraints change, the optimization window moves forward, allowing the plan to adapt to new conditions while maintaining computational efficiency through focused short-term optimization.
2Adaptability or versatility
If a new plan-to-produce cycle is calculated frequently to adapt to changing constraints, then plan optimization is improved, but the process becomes time-consuming
Solution Approach 1:
The patent applies partial optimization by focusing computational efforts only on the near-term optimization window rather than the entire planning horizon. This partial action approach provides sufficient adaptability to changing constraints while avoiding the excessive computational time required for full-cycle re-optimization.
Solution Approach 2:
The system changes the time horizon parameter dynamically, using short optimization windows for frequent updates rather than fixed long-term planning. This parameter adjustment allows the system to respond to constraint changes efficiently without incurring prohibitive computational costs.
3Productivity
If short-term plan-to-produce cycles are used, then computational time is reduced, but the time horizon for production planning is limited
Solution Approach 1:
The patent implements continuous short-term optimization cycles that roll forward through time. Each short-term optimization feeds into the next, creating a continuous planning process that maintains computational efficiency while effectively covering the entire planning horizon through sequential optimization.
Solution Approach 2:
The system employs periodic optimization cycles at regular time intervals rather than continuous long-term optimization. This periodic approach maintains computational efficiency by breaking down the planning horizon into manageable periods, each optimized independently but collectively covering the full planning scope.
Data Source
AI summary
The invention relates to a method for generating an optimized sequence of several tasks to control the production of different products on a production line, including receiving input data related to the products to be produced by the production line, selecting at least one criterion in a set of predetermined criteria using the collected input data, generating a combined matrix by calculating the average of weighted criterion matrixes, applying a genetic algorithm to the combined matrix to derive a set of tasks sequences, selecting an optimized tasks sequence among the determined at least one tasks sequence, and control the production line according to the optimized tasks sequence to produce the products.