<![CDATA[This research presents an integrated approach for optimizing production planning decisions by combining hybrid grid partitioning, rough set analysis, and fuzzy rule generation. The aim is to enhance efficiency and decision-making in the production planning process. The proposed approach addresses the complexities and uncertainties of real-world production environments by providing a comprehensive framework for decision support. The integrated approach begins with hybrid grid partitioning, which offers a structured representation of the decision space. This enables systematic exploration and analysis of different decision variables and their combinations. The subsequent application of rough set analysis reduces the dimensionality of the problem by identifying essential attributes, simplifying the decision-making process and focusing on the most relevant factors. To capture expert knowledge and facilitate adaptive decision-making, fuzzy rule generation is employed. Decision rules based on linguistic terms are generated, allowing for flexible adjustments to production quantities based on linguistic conditions such as demand levels. The combined use of these methodologies provides a holistic and comprehensive framework for optimizing production planning decisions. To demonstrate the effectiveness of the integrated approach, a numerical example is presented. The results indicate that the approach successfully determines optimal production quantities while minimizing production costs, considering capacity constraints, demand requirements, and resource utilization. The integrated approach shows promise in enhancing operational efficiency, improving resource utilization, and aligning with customer demand. The research acknowledges certain limitations, such as simplified assumptions, data availability, and computational complexity. Further validation studies and customization for specific industries are necessary to ensure the practical applicability of the integrated approach. The integrated approach offers a valuable contribution to the field of production planning optimization. By combining multiple methodologies and addressing the complexities of real-world production environments, the approach enhances decision-making and provides a practical framework for organizations seeking to optimize their production processes. Future research directions may focus on addressing the identified limitations and further validating the approach in diverse industrial settings.]]>
