Multi-objective Semiconductor Capacity Planning via Genetic Algorithm
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Solution Overview
Problem
Current semiconductor product capacity planning systems lack the ability to consider multiple objectives, leading to suboptimal decision-making as each department focuses on its own objectives, resulting in inefficiencies and increased costs due to inadequate coordination.
Innovation Solution
A multi-objective semiconductor product capacity planning system and method that uses a data input module, capacity planning module, and computing module to generate planning alternatives by combining machine, product, and order information, employing a multi-objective genetic algorithm and Pareto front method to optimize capacity allocation based on financial and production indices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If each department focuses on its own objectives for capacity planning, then departmental autonomy and simplicity are maintained, but overall coordination efficiency and company-wide optimization deteriorate
Solution Approach 1:
The patent merges the capacity planning functions of multiple departments (sales, production control, manufacturing) into a unified multi-objective optimization system. The system integrates departmental objectives with company-wide goals, allowing simultaneous optimization of revenue, profit, and production efficiency through a centralized computing module that processes inputs from all departments and generates coordinated planning alternatives.
Solution Approach 2:
The capacity planning system is designed with multi-functionality to handle multiple objectives and constraints simultaneously. The computing module can evaluate various planning alternatives based on different criteria (financial indices, production indices, capacity utilization), making the system universally applicable to diverse planning scenarios while maintaining departmental input autonomy.
2Device complexity
If traditional single-objective capacity planning is used, then planning simplicity is maintained, but decision-making quality and optimization capability deteriorate
Solution Approach 1:
The patent segments the complex multi-objective optimization process into manageable components: data input modules for different departments, a computing module for optimization calculations, and evaluation criteria for different objectives. This segmentation allows the system to handle complexity systematically while presenting simplified planning alternatives to decision-makers.
Solution Approach 2:
The system changes the parameters of capacity planning from single-objective to multi-objective optimization. By introducing multiple evaluation criteria (revenue, profit, gross margin, production quantity, capacity utilization) and using multi-objective genetic algorithms, the system transforms the optimization landscape to find Pareto optimal solutions that balance multiple competing objectives simultaneously.
3Loss of time
If capacity planning alternatives are generated without considering financial indices, then planning speed is maintained, but optimization of financial performance deteriorates
Solution Approach 1:
The system performs preliminary calculations and evaluations during the planning generation process itself. The computing module simultaneously calculates multiple financial and production metrics for each planning alternative as they are being generated, rather than performing separate post-processing analyses. This preliminary action integrates financial optimization into the planning speed without requiring additional time-consuming steps.
Solution Approach 2:
The system incorporates feedback mechanisms where planning alternatives are evaluated against multiple objectives and financial indices, and the results feed back into the optimization process. The multi-objective genetic algorithm uses evaluation results to guide subsequent generations of planning alternatives, ensuring that financially optimized solutions are generated efficiently through iterative feedback rather than sequential analysis.
Data Source
AI summary
Disclosure is a multi-objective semiconductor product capacity planning system and method thereof. The system comprises a data input module, a capacity planning module and a computing module. The machine information of the production stations, the product information and the order information are input by the data input module. According to the demand quantity of order, capacity information and product information, the capacity planning module plans a capacity allocation to determine the satisfied quantity of orders. The capacity allocation information is used to form a gene combination by chromosome encoding method. The computing module calculates the gene combination several times to generate numerous candidate solutions by a multi-objective genetic algorithm. The numerous candidate solutions sorts out and generates a new gene combination, and repeats the calculation to form candidate solution set until stop condition is satisfied. The candidate solution set is transformed into numerous suggestive plans as options.