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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
 1 
Search results for , issue "Vol. 15 No. 2 (2021): May: Enterprise Modelling" : 5 Documents clear
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (0.474 KB) | DOI: 10.35335/emod.v15i2.43

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (0.474 KB) | DOI: 10.35335/emod.v15i2.44

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (0.474 KB) | DOI: 10.35335/emod.v15i2.45

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (0.474 KB) | DOI: 10.35335/emod.v15i2.46

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (0.474 KB) | DOI: 10.35335/emod.v15i2.47

Abstract

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.

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