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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. 17 No. 1 (2023): Jan: Enterprise Modelling" : 5 Documents clear
A fuzzy sustainable model for drug supply chain networks during a pandemic Nosatzki Stein Rivest; Hanguir Leiserson Truong
International Journal of Enterprise Modelling Vol. 17 No. 1 (2023): Jan: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (561.019 KB) | DOI: 10.35335/emod.v17i1.68

Abstract

This research focuses on developing a fuzzy sustainable model for drug supply chain networks during a pandemic. The outbreak of a pandemic introduces unprecedented uncertainties and complexities to the drug supply chain, necessitating the integration of sustainability considerations and fuzzy logic techniques into decision-making processes. The proposed model aims to optimize decision variables, such as inventory levels, production capacities, transportation routes, and allocation strategies, while balancing conflicting objectives and addressing sustainability criteria. The model incorporates fuzzy logic to handle imprecise and uncertain inputs, allowing decision-makers to capture qualitative information and expert knowledge. The research emphasizes the importance of sustainability in drug supply chains, encompassing environmental impact, social welfare, and economic viability. Through the use of an optimization framework and a decision support system, stakeholders can make informed decisions considering sustainability criteria and dynamic pandemic conditions. The research contributes to enhancing the resilience, efficiency, and sustainability of drug supply chains during pandemics, facilitating better patient care and community well-being.
A novel stochastic fuzzy decision model for optimizing decision-making in the manufacturing industry Xie Shone Seen; Darvishi Mondragon Ortiz-Barrios; Osei Scott Kant
International Journal of Enterprise Modelling Vol. 17 No. 1 (2023): Jan: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (559.534 KB) | DOI: 10.35335/emod.v17i1.69

Abstract

In unpredictable and imprecise production environments, this research introduces a stochastic fuzzy decision model for the manufacturing industry. Decision-makers can use the stochastic and fuzzy logic model to capture uncertainties, variability, and language representations of industrial factors. The choice problem, fuzzy input variables, and crisp outcome variables are identified to start the research. Linguistic terms related with fuzzy input variables are represented by fuzzy sets and membership functions. Fuzzy rules link fuzzy input variables to crisp output variables based on expert knowledge or historical data. Objective function, restrictions, and fuzzy rules are incorporated into the stochastic fuzzy decision model's mathematical formulation. Decision-makers can maximize outcomes by considering stochastic factors and fuzzy logic with the model. The model uses an optimization technique to find the optimal choice variable values. A numerical example of manufacturing production planning illustrates the model's use. The results show that the stochastic fuzzy decision model may minimize production costs by calculating optimal production quantities depending on demand. The research concludes that the proposed approach helps manufacturing companies make decisions. Decision-makers can use the model to make educated judgments despite uncertainties and inaccurate information. Future study will explore additional aspects and integrate the model into decision support systems or industrial software. In dynamic and uncertain manufacturing contexts, the stochastic fuzzy decision model empowers manufacturing decision-makers to make optimal decisions
Robust learning and optimization in distributionally robust stochastic variational inequalities under uncertainty Sihotang, Hengki Tamando; Michaud, Patrisius; Teresa, Patrys
International Journal of Enterprise Modelling Vol. 17 No. 1 (2023): Jan: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (519.605 KB) | DOI: 10.35335/emod.v17i1.70

Abstract

Robust learning and optimization in distributionally robust stochastic variational inequalities under uncertainty is a crucial research area that addresses the challenge of making optimal decisions in the presence of distributional ambiguity. This research explores the development of methodologies and algorithms to handle uncertainty in variational inequalities, incorporating a distributionally robust framework that considers a range of possible distributions or uncertainty sets. By minimizing the worst-case expected performance across these distributions, the proposed approaches ensure robustness and optimality in decision-making under uncertainty. The research encompasses theoretical analysis, algorithm development, and empirical evaluations to demonstrate the effectiveness of the proposed methodologies in various domains, such as portfolio optimization and supply chain management. The outcomes of this research contribute to the advancement of robust optimization techniques, enabling decision-makers to make reliable and robust decisions in complex real-world systems
Modeling and optimization of multi-altitude leo satellite networks using cox point processes: Towards efficient coverage and performance analysis Titus Gramacy Zhu; Shi-soon Solosa; Periera Maniani
International Journal of Enterprise Modelling Vol. 17 No. 1 (2023): Jan: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (499.268 KB) | DOI: 10.35335/emod.v17i1.71

Abstract

This research focuses on the modeling and optimization of multi-altitude Low Earth Orbit (LEO) satellite networks using Cox point processes to achieve efficient coverage and performance analysis. LEO satellite networks have gained attention for their potential to provide global connectivity with reduced latency and increased network capacity. Accurately modeling the spatial distribution of satellites at different altitudes and optimizing their deployment pose significant challenges. This research proposes a mathematical framework based on Cox point processes to capture the randomness and irregularity of satellite deployments. Optimization algorithms, such as genetic algorithms, are employed to determine the optimal satellite locations, altitude allocation, and network parameters. Performance analysis considers metrics such as coverage probability, signal strength, interference levels, capacity, and quality of service. The research contributes to the development of advanced modeling techniques, optimization algorithms, and performance analysis frameworks, enabling efficient coverage and performance optimization in multi-altitude LEO satellite networks. The numerical examples and discussions illustrate the effectiveness and potential of the proposed approach in enhancing the design and operation of satellite communication systems
Stochastic modeling and performance analysis of multi-altitude LEO satellite networks using cox point processes Panjaitan, Firta Sari
International Journal of Enterprise Modelling Vol. 17 No. 1 (2023): Jan: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (448.732 KB) | DOI: 10.35335/emod.v17i1.72

Abstract

The research focuses on the stochastic modeling and performance analysis of multi-altitude Low Earth Orbit (LEO) satellite networks using Cox point processes. LEO satellite networks have emerged as a promising solution for global connectivity, offering high data rates and low latency. To optimize their performance and resource allocation, accurate modeling and analysis techniques are crucial. This research employs Cox point processes to model the spatial distribution and behavior of satellites at different altitudes within the network. The intensity functions capture the expected number of satellites per unit area at each altitude. Realizations of the Cox point process are generated using Monte Carlo simulations, enabling performance analysis in terms of network connectivity, coverage probability, signal quality, and interference levels. The results provide insights into network behavior and inform network design decisions, including the optimal number of satellites, their altitudes, and their spatial distribution. The research contributes to the advancement of multi-altitude LEO satellite networks, enabling efficient global connectivity and addressing communication needs in various industries and applications

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