Improving Production Planning Decision-Making with Hybrid Grid Partitioning and Rough Set Method for Fuzzy Rule Generation - Georgopoulos Panagiotis Hatsopoulos; Dunnill Cigalas Diakoulakis; Pekka Cromar Kayamba (International Journal of Enterprise Modelling, 2020)
This research proposes a hybrid decision-making framework that combines grid partitioning, rough set method, and fuzzy logic to enhance production planning decision-making. The framework aims to address the complexities and uncertainties associated with production planning processes and provide a structured approach for deriving optimal decisions. The research utilizes a numerical example to illustrate the application of the hybrid framework, where fuzzy rules are generated based on input variables, grid partitioning is employed to discretize the input space, and fuzzy reasoning is utilized to determine the optimal production quantity. The findings highlight the potential of the hybrid approach in improving decision-making outcomes in production planning scenarios. However, limitations such as simplified assumptions, limited scope of validation, and the need for further empirical validation are acknowledged. The research contributes to the scientific understanding of decision support systems in production planning and emphasizes the importance of practical implementation and validation in real-world contexts. Future research can explore the limitations, validate the proposed framework in diverse scenarios, and conduct comparative analyses with existing approaches.