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Tsekos Rokkas Plattner
Schools of University of Piraeus, Greece
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Transportation

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Hybrid Grid Partition and Rought Set Method for Fuzzy Rule Generation in Production Planning Problem Approach Christos Kossyva Broadbent; Tsekos Rokkas Plattner
International Journal of Enterprise Modelling Vol. 14 No. 3 (2020): Sep: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (0.474 KB) | DOI: 10.35335/emod.v14i3.36

Abstract

This research introduces a hybrid grid partition and rough set method for fuzzy rule generation in the production planning problem. The approach combines fuzzy logic, rough set theory, and grid partitioning techniques to address the complexity and uncertainty inherent in production planning decisions. By integrating these techniques, the approach captures the relationships between input variables and production planning decisions, allowing for informed decision-making in dynamic manufacturing environments. The research demonstrates the generation of high-quality fuzzy rules based on membership functions, grid partitioning, and attribute reduction. A numerical example is provided to illustrate the application of the approach, showcasing its potential in improving decision-making accuracy in production planning. However, the research acknowledges limitations such as the simplified scenario, assumption of variable independence, and scalability concerns. Further research is necessary to address these limitations and validate the approach in more realistic and complex production planning scenarios. Overall, the hybrid grid partition and rough set method for fuzzy rule generation offer a promising approach to enhance decision support systems in production planning and contribute to the advancement of manufacturing and supply chain management.
Co-Authors Christos Kossyva Broadbent