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All Journal International Journal of Enterprise Modelling
Mönch Filipe Lio Gonçalves
Daugavpils Universitāte, Latvia
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Transportation

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Optimizing Production Planning Decisions with a Hybrid Grid Partitioning and Rough Set Approach for Fuzzy Rule Generation: A Systematic Framework for Enhanced Operational Efficiency Dallarde Pinto Vluymons; Mönch Filipe Lio Gonçalves
International Journal of Enterprise Modelling Vol. 15 No. 1 (2021): Jan: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (0.08 KB) | DOI: 10.35335/emod.v15i1.41

Abstract

Effective production planning is crucial for organizations to enhance operational efficiency and meet customer demands. This research presents a systematic framework that integrates hybrid grid partitioning, rough set approach, and fuzzy rule generation to optimize production planning decisions. The framework aims to address the challenges of uncertainty, imprecision, and complexity inherent in production planning data. By partitioning the data using a hybrid grid partitioning technique and applying rough set theory, essential features and dependencies are extracted. Fuzzy logic and fuzzy rule generation are then employed to handle uncertainty and capture linguistic relationships between input variables and output decisions. The proposed framework offers a comprehensive decision support system for production planning, considering multiple objectives, constraints, and resource allocations. Through a numerical example, the effectiveness of the framework is demonstrated, showcasing improved operational efficiency and resource utilization. The research contributes to the field by providing a novel approach to optimize production planning decisions and offers practical solutions for organizations seeking to enhance operational efficiency. Future research directions include refining the framework and applying it to specific industry contexts to further validate its effectiveness.
Co-Authors Dallarde Pinto Vluymons