cover
Article Per Year (5 Year)
All
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
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
 1 
Search results for , issue "Vol. 14 No. 2 (2020): May: Enterprise Modelling" : 5 Documents clear
Enhancing Rule Generation in Dataset Classification through Optimized Fuzzy Grid Partitioning: A Performance Optimization Framework Mohammad Nor Amin Hasnan
International Journal of Enterprise Modelling Vol. 14 No. 2 (2020): May: 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.v14i2.28

Abstract

The research aims to enhance rule generation in dataset classification through an optimized fuzzy grid partitioning framework. The proposed framework combines fuzzy grid partitioning, which captures uncertainty and ambiguity in datasets, with an optimization algorithm to improve the accuracy and efficiency of rule generation. The main contributions of this research include enhanced accuracy in dataset classification, improved computational efficiency, and emphasis on interpretability and transparency of the generated rules. The framework achieves improved accuracy by optimizing the fuzzy grid partitioning process to capture underlying patterns in the data. The incorporation of an optimization algorithm reduces the computational complexity, enabling faster classification on large-scale datasets. The generated rules are designed to be understandable to domain experts, enhancing transparency and facilitating decision-making. The research acknowledges certain limitations, such as algorithm dependence and the need for real-world validation. Future research directions include exploring alternative optimization algorithms, conducting extensive evaluations on diverse datasets, and validating the framework's performance in real-world applications. Overall, the proposed framework offers a valuable contribution to the field of data mining and machine learning, providing an effective approach to enhance rule generation in dataset classification.
Optimizing Production Planning in Uncertain Environments: A Fuzzy Goal Programming Approach with Adaptive Metaheuristic Algorithms Jose Sanchez; Gotzee Marion
International Journal of Enterprise Modelling Vol. 14 No. 2 (2020): May: 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.v14i2.29

Abstract

This research focuses on the optimization of production planning in uncertain environments by employing a fuzzy goal programming approach with adaptive metaheuristic algorithms. Uncertainty poses significant challenges in production planning, requiring robust methodologies to handle conflicting objectives and unpredictable factors. In this study, we propose a novel approach that integrates fuzzy logic and metaheuristic algorithms to address these challenges effectively. The fuzzy goal programming framework enables the modeling of imprecise or vague goals and constraints, providing a more accurate representation of uncertainties. The adaptive metaheuristic algorithms offer efficient optimization capabilities by exploring the solution space and adapting to changing circumstances. A mathematical formulation is developed, considering multiple objectives such as minimizing production costs, maximizing production output, minimizing inventory levels, and minimizing deviations from customer demand. The formulation is solved using appropriate metaheuristic algorithms, such as genetic algorithms. A numerical example and a case study are presented to demonstrate the practical application and effectiveness of the proposed approach. The results show that the approach successfully optimizes production planning, achieving the desired levels of satisfaction for each objective in uncertain environments. This research contributes to the field by providing decision-makers with a comprehensive and robust methodology to improve production planning strategies, enhance operational efficiency, and meet customer demands effectively in the face of uncertainty.
Optimizing Production Planning Under Uncertainty: A Fuzzy Goal Programming Approach with Adaptive Metaheuristic Algorithms for Improved Decision-Making Managua Jaramillo Luther; Escritura Perezalonso Según
International Journal of Enterprise Modelling Vol. 14 No. 2 (2020): May: 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.v14i2.30

Abstract

This research addresses the challenge of uncertainty in production planning by proposing a novel approach that combines fuzzy goal programming with stochastic optimization techniques. The integration of these two methodologies provides decision-makers with a comprehensive framework to make improved and robust decisions in the face of uncertainty. Fuzzy goal programming allows decision-makers to express imprecise objectives and constraints, accommodating the inherent vagueness and trade-offs in production planning. Stochastic optimization techniques consider multiple scenarios and their associated probabilities, enabling the optimization of production plans that are robust and near-optimal across different uncertain situations. The proposed approach offers flexibility in decision-making, as decision-makers can express their preferences and goals in a subjective manner while considering uncertainty. The research contributes to the field by providing a systematic framework to manage uncertainty in production planning and improve overall performance in manufacturing organizations. The practical implications of the research are significant, as decision-makers can make informed decisions regarding resource allocation, customer demand fulfillment, and cost optimization. The research findings highlight the effectiveness of the proposed approach and its potential for application in various industry contexts. However, limitations in computational complexity and the need for further refinements are acknowledged. Future research can focus on refining the approach, addressing specific industry challenges, and extending its applicability in real-world production planning problems.
Hybrid Grid Partitioning and Rough Set Method for Fuzzy Rule Generation: Enhancing Dataset Clustering and Interpretability in Data Analysis Nordiyya Ellwanger Hamerkaz
International Journal of Enterprise Modelling Vol. 14 No. 2 (2020): May: 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.v14i2.31

Abstract

This research focuses on the development of a hybrid approach that combines grid partitioning, rough set theory, and fuzzy rule generation to enhance dataset clustering accuracy and improve the interpretability of generated rules in data analysis. The integration of grid partitioning techniques improves clustering accuracy by reducing the search space and efficiently identifying data patterns and relationships. Incorporating rough set theory facilitates attribute reduction, reducing the dimensionality of the dataset and enhancing interpretability. Fuzzy rule generation enables linguistic representation, allowing for human-understandable explanations. The proposed approach addresses the limitations of traditional methods, providing a comprehensive framework for accurate clustering and interpretable rule generation. The significance and potential benefits of the approach are discussed, along with its limitations and future directions. Numerical examples and findings demonstrate the effectiveness of the hybrid approach in enhancing dataset clustering accuracy and interpretability. The research contributes to advancing the field of data analysis by providing a comprehensive framework for accurate and interpretable analysis of complex datasets.
Hybrid Grid Partitioning and Rough Set Method for Enhanced Dataset Clustering and Interpretable Rule Generation in Big Data Analysis Miankoff Gordion JKacker
International Journal of Enterprise Modelling Vol. 14 No. 2 (2020): May: 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.v14i2.32

Abstract

This research introduces a novel approach that combines hybrid grid partitioning and rough set theory for enhanced dataset clustering and interpretable rule generation in big data analysis. The proposed method addresses the challenges of scalability, high dimensionality, and interpretability, which are common in analyzing large and complex datasets. The hybrid approach leverages grid partitioning to efficiently handle large datasets by dividing them into manageable subsets. This enables parallel processing and reduces computational complexity. Additionally, rough set theory is incorporated to identify essential attributes that contribute to cluster formation, thereby reducing the dimensionality of the data and enhancing clustering accuracy. One of the key contributions of this research is the generation of interpretable rules based on the clustering results. By applying rough set-based attribute selection, the method identifies the crucial attributes that determine cluster assignments. These interpretable rules provide valuable insights into the relationships between attributes and clusters, aiding in understanding the underlying patterns in the data. A numerical example is provided to demonstrate the effectiveness of the proposed method. The results show improved clustering accuracy and the generation of clear and interpretable rules based on the dataset attributes. While the research presents significant advancements, it is important to consider the limitations, including potential challenges in generalizability, sensitivity to parameter settings, and computational complexity. Future research should focus on further validation and evaluation of the method on diverse datasets and comparisons with other state-of-the-art clustering algorithms. In conclusion, the hybrid grid partitioning and rough set method offer a promising solution for enhanced dataset clustering and interpretable rule generation in big data analysis. The research contributes to the advancement of data analytics methodologies and provides practical approaches for extracting knowledge from complex datasets, supporting decision-making processes, and enabling better understanding of underlying data patterns.

Page 1 of 1 | Total Record : 5

 1 

Filter by Year

2020 2020


Reset
Filter By Issues
All Issue Vol. 20 No. 1 (2026): January: Publishing Process (Inpress) Vol. 19 No. 3 (2025): September: Enterprise Modelling Vol. 19 No. 2 (2025): May: Enterprise Modelling Vol. 19 No. 1 (2025): January: Enterprise Modelling Vol. 18 No. 3 (2024): September: Enterprise Modelling Vol. 18 No. 2 (2024): May: Enterprise Modelling Vol. 18 No. 1 (2024): Jan: Enterprise Modelling Vol. 17 No. 3 (2023): September: Enterprise Modelling Vol. 17 No. 2 (2023): May: Enterprise Modelling Vol. 17 No. 1 (2023): Jan: Enterprise Modelling Vol. 16 No. 3 (2022): Sep: Enterprise Modelling Vol. 16 No. 2 (2022): May: Enterprise Modelling Vol. 16 No. 1 (2022): Jan: Enterprise Modelling Vol. 15 No. 3 (2021): Sep: Enterprise Modelling: Quantum computing Vol. 15 No. 2 (2021): May: Enterprise Modelling Vol. 15 No. 1 (2021): Jan: Enterprise Modelling Vol. 14 No. 3 (2020): Sep: Enterprise Modelling Vol. 14 No. 2 (2020): May: Enterprise Modelling Vol. 14 No. 1 (2020): Jan: Enterprise Modelling Vol. 13 No. 3 (2019): Sep: Enterprise Modelling Vol. 13 No. 2 (2019): May: Enterprise Modelling Vol. 13 No. 1 (2019): January: Fuzzy Rule Generation More Issue