Improving Production Planning Decisions: A Hybrid Grid Partitioning and Rough Set Approach for Fuzzy Rule Generation in Optimization Models
DOI:
https://doi.org/10.35335/emod.v15i1.42Keywords:
Production planning, Fuzzy rule generation, Optimization models, Hybrid approach, Grid partitioning, Rough set theoryAbstract
This research focuses on enhancing production planning decisions through the use of a hybrid grid partitioning and rough set approach for fuzzy rule generation in optimization models. The aim is to address uncertainty and imprecision inherent in production planning problems and provide an effective decision support framework. The proposed hybrid approach combines grid partitioning and rough set theory to generate accurate and interpretable fuzzy rules. By incorporating fuzzy logic and rough set theory, the approach captures and models uncertain and imprecise data, improving the accuracy of decision-making. The generated fuzzy rules offer valuable insights into the relationships between variables, aiding in understanding and communication with stakeholders. The research demonstrates the advantages of the proposed approach over traditional optimization models by considering uncertain and imprecise data. This leads to improved resource allocation, scheduling, and operational efficiency in production planning. Computational efficiency and practical applicability in real-world manufacturing scenarios are also emphasized. The research acknowledges certain limitations, including simplified assumptions, data availability and quality, scalability, subjectivity in fuzzy rule generation, and limited comparative analysis. These limitations provide avenues for future research to refine and enhance the proposed approach. This research contributes to the field of production planning decision-making by offering a hybrid approach that effectively handles uncertainty and imprecision. The findings have practical implications for manufacturing industries, providing a methodology to enhance resource allocation, scheduling, and overall operational efficiency. Future research can build upon these findings to overcome limitations and further improve the proposed approach for real-world applications.
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