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International Journal of Enterprise Modelling
Published by International Enterprise Integration Association
ISSN : 16939220     EISSN : 29878713     DOI : https://doi.org/10.35335/emod
Core Subject : Economy, Science, Education, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Transportation
The International Journal of Enterprise Modelling serves as a venue for anyone interested in business and management modelling. It investigates the conceptual forerunners and theoretical underpinnings that lead to research modelling procedures that inform research and practice.
Arjuna Subject : Matematika - Matematika Terapan
Articles 5 Documents
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Search results for , issue "Vol. 15 No. 1 (2021): Jan: Enterprise Modelling" : 5 Documents clear
Optimizing Production Planning Decisions with a Hybrid Grid Partitioning and Rough Set Approach for Fuzzy Rule Generation Sykiotis Clifford Markou; David ugajska Hatsopoulos
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 (0.474 KB) | DOI: 10.35335/emod.v15i1.38

Abstract

This research focuses on optimizing production planning decisions using a hybrid grid partitioning and rough set approach for fuzzy rule generation. The aim is to address the challenges associated with uncertainty, complex relationships, and the need for a systematic methodology tailored specifically for production planning. The proposed approach integrates grid partitioning, rough set theory, and fuzzy rule generation to provide decision-makers with a comprehensive framework for generating robust fuzzy rules. The mathematical formulation formulates the optimization problem by considering input variables such as demand and resource availability, output variables representing production quantities, fuzzy membership functions to model linguistic variables, and fuzzy rules to capture relationships. The objective is to minimize deviations between actual and desired outputs while satisfying relevant constraints. A numerical example is presented to illustrate the application of the proposed approach. The results demonstrate improved decision-making, enhanced operational efficiency, and the applicability of the approach to various production planning scenarios. However, limitations in terms of data quality, generalizability to complex production systems, and computational complexity should be considered. Future research should address these limitations and explore real-time adaptability to further enhance the effectiveness of the proposed approach. Overall, this research contributes to the advancement of production planning methodologies by providing a structured framework for handling uncertainty, capturing complex relationships, and optimizing production planning decisions.
Efficient Production Planning Optimization: A Hybrid Grid Partitioning and Rough Set Approach for Fuzzy Rule Generation in Manufacturing Systems Tao Song-slim Shi; Vieral wu Zhang
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 (0.123 KB) | DOI: 10.35335/emod.v15i1.39

Abstract

Efficient production planning is crucial for optimizing resource allocation and improving productivity in manufacturing systems. This research proposes a hybrid grid partitioning and rough set approach for fuzzy rule generation to address the challenges of production planning optimization. The integration of grid partitioning, rough set theory, and fuzzy logic enables a comprehensive analysis of input variables, handling uncertainty, and generating fuzzy rules for decision-making. The grid partitioning technique divides the input space into discrete cells, simplifying the optimization process. Rough set analysis within each cell identifies relevant features and dependencies, enhancing the understanding of the production system's behavior. The generated fuzzy rules capture linguistic relationships between input and output variables, facilitating context-aware decision-making. The evaluation and optimization of the rule set ensure the quality and effectiveness of the decision-making process. The proposed approach offers practical benefits, such as improved resource utilization, cost reduction, and enhanced productivity in manufacturing systems. However, the research acknowledges limitations in terms of the simplified scenario, data availability, computational complexity, parameter sensitivity, and generalizability. Further research is needed to validate and refine the framework in diverse industrial settings. The findings contribute to the advancement of production planning optimization and provide valuable insights for researchers and industry practitioners.
An Integrated Approach for Optimizing Production Planning Decisions: Hybrid Grid Partitioning, Rough Set Analysis, and Fuzzy Rule Generation for Enhanced Efficiency and Decision-Making Ciência Yjensen DE Moreira Pomiszowski
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 (0.123 KB) | DOI: 10.35335/emod.v15i1.40

Abstract

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.
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.
Improving Production Planning Decisions: A Hybrid Grid Partitioning and Rough Set Approach for Fuzzy Rule Generation in Optimization Models Madoza Noonyan Savastjanov
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 (0.123 KB) | DOI: 10.35335/emod.v15i1.42

Abstract

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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