Computer Assembly Line Balancing Using Migration Genetic Transfer
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Solution Overview
Problem
Existing assembly line balancing methods struggle to efficiently adapt to frequent changes in production processes due to high sensitivity to initial values and parameters, leading to sub-optimal solutions and prolonged execution times, especially in computer assembly where diverse and personalized orders are common.
Innovation Solution
A migration genetic algorithm-based method that utilizes a priority relationship matrix to initialize populations, reproduce high-quality feasible solutions, and adjust them to meet the requirements of different production lines, optimizing the assembly line balancing by reducing sensitivity to initial values and parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional genetic algorithms are used to solve assembly line balancing problems, then the method is simple and easy to understand, but the obtained solution is often sub-optimal and convergence speed is slow
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing optimal or near-optimal solutions for common assembly line configurations in a database before actual production needs arise. When a new assembly line balancing problem occurs, the system queries the database for pre-computed solutions, significantly reducing convergence time while maintaining high solution quality.
Solution Approach 2:
The patent uses copying by creating a database of historical optimal solutions from previous assembly line configurations. Instead of solving each problem from scratch, the system copies and adapts pre-computed solutions to current problems, achieving fast convergence without sacrificing optimality.
2Measurement precision
If mathematical programming methods are used to obtain optimal analytical solutions, then the solution accuracy is high, but the computational amount is too large
Solution Approach 1:
The patent copies pre-computed optimal solutions from a database into the current problem space. By retrieving and adapting historical optimal solutions rather than recalculating them, the system achieves high solution accuracy without the excessive computational burden of re-running complex mathematical programming algorithms.
Solution Approach 2:
The system performs the computationally intensive mathematical programming calculations in advance during offline processing, storing the results in a database. During online operation, it simply retrieves and adapts these pre-computed solutions, separating the heavy computational work from the actual problem-solving time.
3Adaptability or versatility
If intelligent optimization algorithms are used to solve NP-complete problems, then the algorithm can efficiently solve complex problems, but parameters are difficult to control, convergence speed is slow, and execution time is long
Solution Approach 1:
The patent copies historical optimal solutions from a database of previously solved NP-complete assembly line balancing problems. This approach maintains the ability to handle complex NP-complete problems while dramatically reducing execution time by avoiding redundant computational exploration of solution spaces.
Solution Approach 2:
The system performs complex optimization calculations in advance during offline processing, storing results in a database. When new problems arise, it retrieves pre-computed solutions, separating the time-consuming optimization process from the actual problem-solving time and enabling fast response to diverse NP-complete problems.
Data Source
AI summary
A second type computer assembly line balancing optimization method based on a migration genetic algorithm, and related to the technical field of assembly line balancing. The method uses assembly experience of similar assembly lines, the feasible solution set of the known assembly lines is transferred to the initial solution set of the assembly line balancing problem to be optimized, due to the migration of high-quality feasible solutions. The method can effectively reduce the sensitivity of the algorithm performance to the initial value and parameters, and improve the lower limit of the local optimal feasible solution of the heuristic algorithm to solve the assembly line balancing problem.