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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 6 Documents
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Search results for , issue "Vol. 16 No. 3 (2022): Sep: Enterprise Modelling" : 6 Documents clear
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (535.788 KB) | DOI: 10.35335/emod.v16i3.63

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (530.357 KB) | DOI: 10.35335/emod.v16i3.64

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (530.686 KB) | DOI: 10.35335/emod.v16i3.65

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (457.478 KB) | DOI: 10.35335/emod.v16i3.66

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (556.21 KB) | DOI: 10.35335/emod.v16i3.67

Abstract

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
Effectiveness of Ultrasonic Frequencies on the Behavior and Migration Patterns of Rice Field Rats (Rattus argentiventer) Sihotang, Hengki Tamando; Sihotang, Jonhariono; Simbolon, Romasinta
International Journal of Enterprise Modelling Vol. 16 No. 3 (2022): Sep: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/int.jo.emod.v16i3.163

Abstract

Rat infestation by Rattus argentiventer remains a serious problem in irrigated rice fields, causing significant yield losses and threatening sustainable rice production. Conventional control methods rely heavily on chemical rodenticides, which pose environmental risks and show declining long-term effectiveness. Ultrasonic deterrent technology has been proposed as an alternative; however, its effectiveness in open-field agricultural environments remains inconsistent and poorly understood. This study aims to analyze the behavioral and migration responses of rice field rats to different ultrasonic frequency ranges to clarify the mechanisms underlying ultrasonic deterrence. A field-based experimental design was applied using paired treatment and control plots, with ultrasonic frequencies ranging from 20 to 40 kHz. Rat activity and movement were monitored through camera traps and motion sensors, and spatial behavior was analyzed using activity reduction rates, migration distance, and path deviation indices. The results indicate a clear frequency-dependent response, with ultrasonic exposure at 30–35 kHz producing the strongest avoidance behavior and directional displacement. These findings suggest that ultrasonic deterrence primarily induces spatial displacement rather than population elimination and provide important implications for the development of adaptive ultrasonic–IoT systems to support smart and sustainable pest management in rice agriculture.

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