Optimizing Manufacturing Operations: A Fuzzy Associative Memory-Based Decision Support System for Production Quantity Determination
Keywords:
Fuzzy Associative Memory (FAM), Decision Support System (DSS), ProductionQuantity Determination, Manufacturing Optimization, Fuzzy Logic in Decision-MakingAbstract
This research introduces a novel approach to optimizing production quantity determinations within manufacturing through the integration of a Decision Support System (DSS) based on the Fuzzy Associative Memory (FAM) method. The study explores the application of fuzzy logic principles and linguistic variables to enhance decision-making accuracy and efficiency in dynamic production environments. Leveraging the adaptability and robustness of FAM, the developed DSS accommodates uncertainty, complexity, and varied input parameters, offering nuanced and agile decision support capabilities. The research methodology involves defining linguistic variables for demand, resource availability, and production quantity, along with designing fuzzy sets and membership functions. The FAM model integrates these linguistic variables with IF-THEN fuzzy rules, capturing the intricate relationships between inputs and outputs. The DSS architecture incorporates this model, providing decision-makers with an intuitive interface for visualizing, analyzing, and selecting optimal production quantities. Results demonstrate the superiority of the FAM-based DSS over traditional methods, showcasing enhanced accuracy in production quantity estimations, efficient resource utilization, and agile responsiveness to changing demand scenarios. The system's adaptability and robustness contribute to mitigating risks associated with overproduction or underproduction, thereby optimizing inventory levels and reducing operational costs.
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