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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 114 Documents

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Data envelopment analysis for stochastic production and supply chain planning Hengki Tamando Sihotang; Patrisia Teresa Marsoit; Kouvelis Geovany Ortizan
International Journal of Enterprise Modelling Vol. 16 No. 3 (2022): Sep: Enterprise Modelling
Publisher : International Enterprise Integration Association

This research presents a stochastic Data Envelopment Analysis (DEA) model for production and supply chain planning. The objective is to evaluate the efficiency of decision-making units (DMUs) in a system considering the stochastic nature of inputs and outputs. The proposed model incorporates uncertainty by assuming normal distributions for the stochastic variables. The model formulates a linear programming problem to maximize the efficiency scores of DMUs subject to constraints that ensure the efficiency of the system. The weights assigned to DMUs and input variables provide insights into their relative importance. A numerical example is presented to demonstrate the application of the model, and the results highlight the efficiency scores and weights for the DMUs. The findings contribute to improving decision-making in production and supply chain systems under uncertain conditions. The developed model offers a practical tool for evaluating efficiency and identifying areas for improvement in real-world systems. Further research can explore extensions and variations of the model to enhance its applicability in different contexts

Enhancing vehicle routing problem solutions through deep reinforcement learning and graph neural networks Zhou Cien Tien; Joe Qi-lee
International Journal of Enterprise Modelling Vol. 16 No. 3 (2022): Sep: Enterprise Modelling
Publisher : International Enterprise Integration Association

The Vehicle Routing Problem (VRP) involves finding optimal routes for a fleet of vehicles to serve a set of clients while minimizing costs or optimizing efficiency. Scalability and uncertainty handling are issues with traditional VRP solutions. This study integrates Deep Reinforcement Learning (RL) with Graph Neural Networks (GNNs) to improve VRP solutions. Deep RL algorithms let agents learn optimal decision-making rules by interacting with the environment, whereas GNNs capture the VRP's graph representation's spatial and structural relationships. This research uses deep RL and GNNs to improve VRP solutions. The project intends to create an agent that can reason about customer, vehicle, and depot interactions and make educated routing decisions depending on the problem state by integrating deep RL agents with GNN models. Formulating the problem, preprocessing the data, constructing state and action representations, defining reward functions, training the deep RL agent and GNN models, and assessing the proposed strategy using benchmark VRP datasets. The merged deep RL-GNN technique improves VRP solutions. Optimized routing reduces travel expenses, improves resource use, and boosts efficiency. This research shows how deep RL and GNNs can overcome the limits of classic optimization methods for vehicle routing optimization. The findings emphasize the need of integrating advanced machine learning techniques into the VRP domain, enabling more effective and scalable real-world vehicle routing systems

Optimizing production planning efficiency and sustainability using multi-objective decision making and goal programming Eileen Haas Jung; Aschari Reinhard Laamanen
International Journal of Enterprise Modelling Vol. 16 No. 3 (2022): Sep: Enterprise Modelling
Publisher : International Enterprise Integration Association

Optimization of production planning efficiency and sustainability is crucial for organizations aiming to achieve operational excellence while minimizing their environmental footprint. This research proposes a novel approach that combines Goal Programming and Multiple Criteria Decision Making (MCDM) techniques to address the multi-objective nature of production planning. The study develops a mathematical formulation that considers objectives such as production efficiency, cost minimization, environmental impact reduction, and adherence to sustainability targets. A decision support system is designed to assist decision-makers in evaluating trade-offs and identifying the most suitable compromise solution. The research employs a numerical example to demonstrate the effectiveness of the proposed approach, showcasing how production quantities and sustainability practices can be optimized. The results highlight the ability of the approach to strike a balance between efficiency and sustainability, providing decision-makers with a comprehensive framework to make informed decisions aligned with sustainability goals. This research contributes to the existing literature by offering a practical methodology that enhances production planning processes, leading to more sustainable and efficient operations

Next-generation air routing: Integrating AI, multi-objective optimization, and collaborative decision making for efficient and sustainable flight planning Dominković Rosenow; Tsao Tao Lee; Zhu Xue Li
International Journal of Enterprise Modelling Vol. 16 No. 3 (2022): Sep: Enterprise Modelling
Publisher : International Enterprise Integration Association

Next-generation air routing aims to revolutionize flight planning by integrating artificial intelligence (AI), multi-objective optimization, and collaborative decision making to improve efficiency and sustainability. This research investigates the application of these techniques to optimize flight routes, minimize fuel consumption, reduce flight time, and enhance overall operational efficiency. The research develops a mathematical formulation model based on binary decision variables for aircraft routing, considering constraints such as airspace capacity, departure time, time windows, and route connectivity. The formulated model is solved using optimization algorithms to obtain optimized routing decisions. The results demonstrate the potential benefits of next-generation air routing, including reduced fuel consumption, improved flight time, efficient airspace capacity utilization, and logical route connectivity. The research contributes to the ongoing efforts in the aviation industry to address challenges related to efficiency, sustainability, and capacity management in flight planning. The findings provide insights for industry practitioners and policymakers to develop advanced systems and decision support tools for more efficient and sustainable flight operations

A hybrid approach integrating goal programming, multiple criteria decision making, and dynamic decision-making for production planning Naliaka Jacobs Yannis; Giret Jia Zare; Fang Lu-Tien Ceng
International Journal of Enterprise Modelling Vol. 16 No. 3 (2022): Sep: Enterprise Modelling
Publisher : International Enterprise Integration Association

This study suggests combining Goal Programming, Multiple Criteria Decision Making (MCDM), and Dynamic Decision-Making to solve production planning difficulties. Production planning entails balancing conflicting goals and dynamic circumstances when allocating resources, scheduling production, and managing inventory. The hybrid approach provides decision-makers with a comprehensive and adaptive framework that balances conflicting objectives, analyzes options using numerous criteria, and accounts for the dynamic production environment. Goal Programming helps solve the production planning challenge. MCDM methods like AHP or TOPSIS analyze and rank various production plans based on multiple factors. Dynamic Decision-Making methods like stochastic programming or simulation optimization accommodate for demand, supply, and other uncertainties in the production environment. A numerical example shows how the hybrid approach develops an optimal production plan by minimizing deviations from desired targets. Decision-makers can evaluate objective priorities and their effects on the solution by altering objective weights in sensitivity analysis. The hybrid approach can handle conflicting objectives, evaluate options using numerous criteria, and adapt to a dynamic production environment, according to studies. The suggested approach provides decision-makers with a comprehensive framework for efficient and successful production planning, adding to current information. Applying the hybrid method to real-world case studies, addressing supply chain dynamics and sustainability, and using AI and machine learning to improve decision-making are future research objectives. Production planning using Goal Programming, MCDM, and Dynamic Decision-Making seems promising. It helps manufacturers optimize resource allocation, customer happiness, and operational efficiency

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

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

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

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

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

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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Zeehimin Huang Ping

Contact Email
internationalenterpriseintegra@gmail.com

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+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

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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