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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 121 Documents

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Optimizing Supply Chain Resilience through Robust Production Planning Zhank Loh Blackhurst
International Journal of Enterprise Modelling Vol. 15 No. 2 (2021): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

This study focuses on improving supply chain resilience through careful production planning. The study helps to the understanding of how firms can increase their ability to endure disruptions and improve overall supply chain performance by generating a mathematical framework and offering a numerical example. The study emphasizes the need of factoring in aspects such as demand uncertainty, manufacturing capacity, inventory management, and supply chain disruption costs when making decisions. The proposed mathematical concept enables firms to reduce costs, satisfy demand, effectively allocate resources, and improve supply chain resilience. It is critical to recognize the research's limitations. When applying the findings to real-world supply chain contexts, the simplified assumptions, limited generalizability, data availability and quality concerns, computational complexity, subjective trade-offs, and lack of validation must all be considered. Future research should concentrate on overcoming these constraints and improving the model to account for the intricacies and dynamism of various supply chain situations. Empirical validation and case studies using real-world data would provide further insights and improve the research findings' applicability. This study adds to the body of knowledge in supply chain management and optimization by presenting a paradigm for robust production planning that takes supply chain resilience into account. Businesses can increase their ability to respond, manage risks, and preserve operational continuity in the event of interruptions by optimizing production quantities, inventory levels, and supply allocation. In today's increasingly turbulent business climate, achieving a robust supply chain improves customer happiness, lowers costs, and improves overall business performance.

Optimizing Resource Allocation and Efficiency in Production Planning for Sustainable Manufacturing: A Case Study in the Post-Pandemic Economy Kache Ness Peck; Tersine Heilig de Sousa
International Journal of Enterprise Modelling Vol. 15 No. 2 (2021): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

Sustainable manufacturing faces new difficulties and opportunities in the post-pandemic economy. Given the changing manufacturing scene, this research addresses production planning resource allocation and efficiency. The study incorporates sophisticated technology, lean manufacturing, data analytics, and supply chain collaboration. A mathematical model for resource allocation and efficiency optimization starts the investigation. The model incorporates production capacity, demand fulfillment, unit costs, and resource efficiencies. An objective function minimizes production cost while meeting demand and resource capacity limits. The model supports optimization and decision-making. Research shows that modern technologies improve resource allocation and efficiency. Automation, robots, and AI algorithms improve operations, minimize errors, and enable data-driven decision-making. Lean manufacturing, including waste reduction and just-in-time inventory management, improves resource usage and efficiency. Data analytics aids production planning decision-making. Real-time production bottleneck, energy consumption, and resource utilization data informs proactive modifications. Data analytics improve decision-making. Sustainable manufacturing requires supply chain collaboration. Stakeholder collaboration, synchronized planning, and information exchange align production plans, reduce disruptions, and boost supply chain efficiency. The study promotes collaboration for long-term sustainability. The study admits numerous limitations but offers significant observations and recommendations. Context-specific application, data availability and quality, and real-world production system complexity are examples. These limitations should be addressed in future study. This research optimizes resource allocation and production planning for sustainable post-pandemic manufacturing. Advanced technology, lean manufacturing, data analytics, and supply chain collaboration can improve resource utilization, cut costs, and contribute to environmental sustainability. The findings support real-world industrial strategy development.

Hybrid Grid Partitioning and Fuzzy Goal Programming Model To Production Planning Problems Approach Poongothai Scott Özcan; Kouvelis Hocine Garetti
International Journal of Enterprise Modelling Vol. 15 No. 2 (2021): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

The Hybrid Grid Partitioning and Fuzzy Goal Programming model is a novel method for solving production planning issues that is presented in this study. The model combines the spatial characteristics-capturing Grid Partitioning approach with the handling fuzzy goals and constraints-handling Fuzzy Goal Programming. The study offers a mathematical formulation and a numerical example to show how well the model performs in terms of capacity considerations, optimizing resource allocation, and incorporating subjective preferences using fuzzy membership functions. The results emphasize the model's potential benefits for optimizing resource utilization, handling uncertainties, and production planning decision-making. However, there are certain drawbacks, including oversimplified assumptions, scalability issues, and insufficient validation. Future studies should look at these issues and determine whether the model can be used in actual production settings. Overall, the Hybrid Grid Partitioning and Fuzzy Goal Programming model provides a thorough framework for production planning that incorporates geographic factors and fuzziness in the optimization process.

Hybrid Grid Partitioning and Fuzzy Goal Programming Model To Manufacturing Systems Modelling Paydar Aouni Castaño
International Journal of Enterprise Modelling Vol. 15 No. 2 (2021): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

This research develops a Hybrid Grid Partitioning and Fuzzy Goal Programming manufacturing systems model. The model optimizes resource allocation while considering system spatial layout and production target imprecision. The model supports industrial system decision-making by integrating grid partitioning with fuzzy goal fulfilment. The model's binary resource allocation and fuzzy goal fulfillment decision variables are mathematically formulated. It optimizes resource allocation costs while meeting ambiguous goal constraints. The model estimates grid cell satisfaction levels for numerous manufacturing goals, such as cost minimization and production rate maximization, depending on production rates. The research shows that Hybrid Grid Partitioning with Fuzzy Goal Programming optimizes resource allocation and system performance. The model shows localized resource distribution within grid cells, including spatial limits and workstation distances. Fuzzy goal satisfaction tackles manufacturing goal imprecision and unpredictability, allowing decision-makers to adjust to changing market conditions and optimize goal satisfaction. The simplified production system and computational complexity of the model are constraints of the research. Real-world case studies must test the model's efficacy and applicability, and further study must examine scalability, parameter sensitivity, and goal integration. The research advances manufacturing systems modeling by considering spatial allocation and fuzzy goal fulfilment. In dynamic and uncertain contexts, the hybrid model optimizes resource allocation, efficiency, and manufacturing system competitiveness.

A Hybrid Grid Partitioning and Fuzzy Goal Programming Model for Enhanced Performance and Decision Support Trivedi Pieters Provost; Mansouri Knüsel Heilig
International Journal of Enterprise Modelling Vol. 15 No. 2 (2021): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

This study introduces an innovative Hybrid Grid Partitioning and Fuzzy Goal Programming Model for Enhanced Performance and Decision Support. The proposed model combines grid partitioning and fuzzy goal programming techniques in order to address the challenges of complex systems and optimize performance while taking into account subjective preferences and uncertainties. Using grid partitioning, the hybrid model divides the problem space into smaller grid cells, facilitating localized analysis and efficient processing. Fuzzy goal programming handles ambiguous objectives and uncertain constraints, providing decision-making flexibility and robustness. The effectiveness of the model is demonstrated through a numerical example, highlighting enhanced performance and decision support for complex problems. The applicability of the hybrid paradigm to diverse fields, including transportation, logistics, and resource management, is discussed. The study also acknowledges limitations, such as computational complexity and the need for validation in the actual world. In the future, it will be necessary to resolve these limitations and improve the hybrid model. Overall, the research advances optimization methodologies and decision support systems by providing a comprehensive framework for confronting complex systems and facilitating well-informed decision making.

Quantum computing for manufacturing and supply chain optimization: enhancing efficiency, reducing costs, and improving product quality Weinberg Jiang Chen; Griffin Schworm Marcus; D'Souza Leesburg
International Journal of Enterprise Modelling Vol. 15 No. 3 (2021): Sep: Enterprise Modelling: Quantum computing
Publisher : International Enterprise Integration Association

The research explores the application of quantum computing to manufacturing and supply chain optimization in an effort to increase productivity, reduce costs, and improve product quality. Quantum algorithms, specifically the Quantum Approximate Optimization Algorithm (QAOA), are developed and evaluated to solve complex optimization problems in these domains. Quantum computing approaches are contrasted with traditional optimization techniques to demonstrate the potential advantages of quantum algorithms in terms of solution quality and working time efficiency. Practical implementation considerations of data availability, algorithm scalability, and system integration are also discussed. This research shows that quantum algorithms can effectively optimize production scheduling, resource allocation, and supply chain management, resulting in shorter production schedules and improved operational performance. This research recognizes the limitations of current quantum hardware, the complexity of the problem domain, and the difficulty of implementation. Despite these limitations, this research lays the foundation for further investigation and innovation in quantum computing for manufacturing and supply chain optimization, highlighting the potential for long-term transformative effects on industrial operations.

Quantum computing and supply chain optimization: addressing complexity and efficiency challenges Ming-Lang Tun Hwang; Wi-Lang Collin; Lee Sen Wang-xu
International Journal of Enterprise Modelling Vol. 15 No. 3 (2021): Sep: Enterprise Modelling: Quantum computing
Publisher : International Enterprise Integration Association

Quantum computing is used to address supply chain optimization complexity and efficiency. Multiple locations, time periods, transportation expenses, facility opening costs, production capacity, and demand fulfillment requirements complicate supply chains. Supply chain optimization's complexity and huge solution areas challenge traditional optimization methods. Quantum algorithms can efficiently explore bigger solution areas in quantum computing. Starting with problem identification, this research reviews quantum computing and supply chain optimization literature. The supply chain optimization problem is modeled mathematically to incorporate transportation, facility opening, production, and cost. Binary choice factors and constraints ensure demand fulfillment, facility capacity limitations, and flow balance. The mathematical theory is applied numerically. The example addresses three locations, two time periods, transportation costs, demand amounts, production capacity, and facility opening costs. A proper optimization solver optimizes the decision variables to reduce total cost while meeting demand and making efficient supply chain decisions. The supply chain optimization model reduces costs and informs transportation, facility opening, and production decisions. The numerical example shows how quantum computing may optimize supply chain topologies and reduce costs. The study explains the findings, highlights gaps in the literature, and stresses the need for more research to bridge theory and practice. This study advances supply chain optimization with quantum computing. It shows how quantum computing might improve supply chain network decision-making, efficiency, and cost.

Quantum computing for production planning Fristi Riandari; Aisyah Alesha; Hengki Tamando Sihotang
International Journal of Enterprise Modelling Vol. 15 No. 3 (2021): Sep: Enterprise Modelling: Quantum computing
Publisher : International Enterprise Integration Association

This research investigates the potential of quantum computing in production planning and addresses the limitations of conventional computing approaches. Traditional methods have been partially effective, but they struggle to solve complex optimization problems, accurately predict demand, and manage supply chains efficiently. The unique computational capabilities of quantum computing offer promising solutions to surmount these obstacles and revolutionize production planning processes. This study seeks to bridge the gap between quantum computing and production planning by analyzing the benefits, limitations, and challenges of its applicability in this field. It proposes customized algorithms and methodologies for leveraging quantum computation to enhance production planning efficiency, cost reduction, and decision-making processes. The research demonstrates the potential of quantum algorithms to minimize total production costs while appeasing demand and resource constraints through a numerical example and mathematical formulation. The results emphasize the advantages of quantum computing in terms of cost reduction, enhanced efficiency, and scalability. Comparisons with conventional methods illuminate the benefits and drawbacks of quantum computing in production planning. This research contributes to the development of novel strategies to improve production planning efficiency, lower costs, and enhance decision-making processes, allowing organizations to leverage quantum computing for optimized production operations

Quantum-inspired fuzzy genetic programming for enhanced rule generation in complex data analysis Patrisius Michaud Felix Marsoit
International Journal of Enterprise Modelling Vol. 15 No. 3 (2021): Sep: Enterprise Modelling: Quantum computing
Publisher : International Enterprise Integration Association

Rule generation in complex data analysis tasks poses challenges in terms of accuracy and interpretability. This research proposes a novel approach called Quantum-Inspired Fuzzy Genetic Programming (QIFGP) that integrates concepts from fuzzy logic, genetic programming, and quantum-inspired computing to address these challenges. The QIFGP model enhances the exploration of the solution space, increases the diversity of generated rules, and improves the accuracy and interpretability of the generated rules. The model is applied to a credit risk assessment problem, and the results are compared with traditional fuzzy logic-based approaches and genetic programming without quantum-inspired features. The experimental results demonstrate that the QIFGP model outperforms the baseline methods in terms of accuracy, achieving an accuracy of 87.5%. The generated rules exhibit a high level of interpretability, providing linguistic labels that capture meaningful relationships between the input features and risk classes. The incorporation of quantum-inspired features enables efficient exploration of the solution space while maintaining computational efficiency. The generalizability and robustness of the QIFGP model are demonstrated through consistent performance across multiple experiments and datasets. The QIFGP model offers a promising approach for enhanced rule generation in complex data analysis tasks, with potential applications in various domains where accurate and interpretable rule generation is crucial.

Quantum computing approach in uncertain data optimization problem for vehicle routing problem Patrisia Teresa Marsoit; Liu Wang Zhang; Deodoro Lakonde; Firta Sari Panjaitan
International Journal of Enterprise Modelling Vol. 15 No. 3 (2021): Sep: Enterprise Modelling: Quantum computing
Publisher : International Enterprise Integration Association

This research addresses the Vehicle Routing Problem (VRP) with uncertain data and proposes a novel approach using quantum computing techniques. The problem involves optimizing vehicle routes considering uncertain customer demands, time windows, and vehicle capacities. We formulate the problem mathematically and develop an algorithmic framework to tackle it. The approach incorporates multiple scenarios based on the uncertainty distribution and selects the one with the minimum cost to optimize the vehicle routes. Through a numerical example, we demonstrate the effectiveness of the proposed approach in generating optimal routes that minimize the total distance traveled by the vehicles. The results highlight the solution quality, adaptability to uncertainty, and potential benefits in terms of cost reduction and resource utilization. While the computational efficiency of quantum computing approaches is a consideration, this research provides a promising direction for addressing uncertain optimization problems in logistics and transportation. Future research should focus on scalability and refinement of the algorithm to further enhance its applicability in real-world scenarios.

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Zeehimin Huang Ping

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internationalenterpriseintegra@gmail.com

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+6281360000791

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internationalenterpriseintegra@gmail.com

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

Location
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INDONESIA

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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

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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