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International Journal of Enterprise Modelling

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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 109 Documents

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Hybrid Grid Partitioning, Rough Set Theory, and Fuzzy Rule Generation for Enhanced Association Rule Mining on Complex Datasets Neeta Grant Caribbean
International Journal of Enterprise Modelling Vol. 14 No. 3 (2020): Sep: Enterprise Modelling
Publisher : International Enterprise Integration Association

Association rule mining plays a crucial role in extracting valuable insights and patterns from complex datasets. However, traditional association rule mining algorithms often face challenges in accurately discovering relevant rules due to the complexity, uncertainty, and vagueness inherent in such datasets. In this research, we propose an integrated approach that combines hybrid grid partitioning, rough set theory, and fuzzy rule generation to enhance association rule mining on complex datasets. First, hybrid grid partitioning is employed to divide the data space into a set of refined grid cells, allowing for more precise representation of the dataset's structure. Next, rough set theory is utilized to handle uncertainty and vagueness by computing lower and upper approximations of concepts. This enables the identification of objects that share similar condition attribute values and improves the robustness of rule generation. Additionally, fuzzy rule generation is incorporated to capture nuanced relationships and patterns within the dataset. Fuzzy logic is employed to represent imprecise and subjective concepts, facilitating the discovery of deeper insights and enhancing the comprehensibility of the generated rules. The proposed approach contributes to the accuracy, interpretability, and relevance of association rules in complex datasets. By integrating multiple techniques, it addresses the limitations of traditional algorithms and provides a comprehensive framework for knowledge discovery. Experimental evaluations demonstrate the effectiveness of the proposed approach in enhancing rule discovery accuracy and interpretability compared to traditional methods. Although some limitations, such as scalability and parameter sensitivity, need to be addressed, the research's findings highlight the potential of the integrated approach for extracting valuable insights from complex datasets. The proposed methodology has broad applicability across various domains and can empower decision-making processes in areas such as market basket analysis, customer behavior analysis, bioinformatics, and web mining. Future research can focus on addressing the identified limitations and further validating the approach's effectiveness in real-world scenarios.

Complex Data Set Problem Solving With Hybrid Grid Partition And Rought Set Method For Fuzzy Rule Generation As A Solution Mikstas Romantė Serksnas; Bučienė Naujienos Leganovic
International Journal of Enterprise Modelling Vol. 14 No. 3 (2020): Sep: Enterprise Modelling
Publisher : International Enterprise Integration Association

This research proposes a novel approach that combines hybrid grid partitioning and the rough set method for fuzzy rule generation to address the challenges posed by complex data set problem-solving. The approach aims to improve data representation, handle uncertainty, identify relevant features, extract dependency rules, and generate accurate fuzzy rules. The hybrid grid partitioning technique probabilistically assigns data points to cells based on local density, enhancing data representation and capturing variations in data density. The rough set method is applied within each cell to identify relevant features and extract dependency rules, considering the uncertainty and incompleteness present in the data. Fuzzy logic is incorporated to generate linguistically interpretable fuzzy rules that capture the complex relationships within the data set. The proposed approach offers an effective solution for complex data analysis, enabling enhanced decision-making, prediction accuracy, and understanding across various domains. However, the approach has limitations, including sensitivity to parameter selection, computational complexity, assumption of independence, interpretability of fuzzy rules, and generalizability to diverse domains. Further research and refinement are necessary to address these limitations and enhance the approach's performance and applicability. Overall, this research contributes to the field of complex data analysis by providing a comprehensive approach for problem-solving, with the potential to advance decision-making and understanding in complex data sets.

An Enhanced Hybrid Grid Partitioning and Rough Set Approach for Fuzzy Rule Generation in Complex Dataset Problem Szolga Mihaela Munteanu; Makeithappen Daniela Ioan
International Journal of Enterprise Modelling Vol. 14 No. 3 (2020): Sep: Enterprise Modelling
Publisher : International Enterprise Integration Association

This research presents an enhanced hybrid grid partitioning and rough set approach for fuzzy rule generation in complex datasets. Complex datasets pose challenges due to their high dimensionality, nonlinearity, and uncertainty, which traditional analysis techniques struggle to address. Fuzzy rule-based systems offer a promising solution, but generating effective fuzzy rules becomes increasingly difficult as the dataset complexity increases. To overcome these challenges, we propose a methodology that integrates hybrid grid partitioning and rough set theory. The hybrid partitioning technique captures global and local patterns, while rough set theory handles uncertainty and incomplete information. The approach generates accurate and interpretable fuzzy rules by optimizing the rule set's size and balancing accuracy and interpretability. Experimental evaluations demonstrate the approach's effectiveness in terms of rule accuracy, interpretability, and computational efficiency. The generated fuzzy rules provide valuable insights into complex relationships, aiding decision-making processes in various domains. The research contributes to the advancement of fuzzy rule generation techniques for complex datasets and offers a practical solution for knowledge extraction in complex systems.

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

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.

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 Vol. 14 No. 3 (2020): Sep: Enterprise Modelling
Publisher : International Enterprise Integration Association

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.

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

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

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

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

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

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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Zeehimin Huang Ping

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Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Zeehimin Huang Ping

Contact Email internationalenterpriseintegra@gmail.com

Phone +6281360000791

Journal Mail Official internationalenterpriseintegra@gmail.com

Editorial Address Jl. Raya Abepura, Wahno, Kec. Abepura, Kota Jayapura, Papua 99926, Indonesia

Location Kota jayapura, P a p u a INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
Zeehimin Huang Ping

Contact Email
internationalenterpriseintegra@gmail.com

Phone
+6281360000791

Journal Mail Official
internationalenterpriseintegra@gmail.com

Editorial Address
Jl. Raya Abepura, Wahno, Kec. Abepura, Kota Jayapura, Papua 99926, Indonesia

Location
Kota jayapura,

P a p u a

INDONESIA