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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. 16 No. 1 (2022): Jan: Enterprise Modelling" : 5 Documents clear
Hybrid approach for adaptive fuzzy grid partitioning and rule generation using rough set theory Pa Liu Zheng; Liu Wang Zhang; Li Wang Cheng; Koscik Xue Huang
International Journal of Enterprise Modelling Vol. 16 No. 1 (2022): Jan: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (471.649 KB) | DOI: 10.35335/emod.v16i1.53

Abstract

This research proposes a hybrid approach for adaptive fuzzy grid partitioning and rule generation using rough set theory to address the problem of customer segmentation based on purchasing behavior. The objective is to minimize the fuzziness of the partitioning while maximizing the accuracy and interpretability of the generated rules. The research utilizes a dataset consisting of customer transactions, including demographics, purchase details, and satisfaction ratings. The fuzzy grid partitioning process divides the customer space into grid cells, representing different segments. Fuzzy membership values are assigned to data points based on their association with each grid cell. Rough set theory is employed for attribute reduction, identifying the most relevant attributes for customer segmentation. Rule induction algorithms generate rules that capture the patterns and dependencies among customer attributes and their association with specific grid cells. The hybrid approach combines the advantages of adaptive fuzzy grid partitioning and rough set-based rule generation. The optimization process adjusts fuzzy membership values and refines the generated rules to improve accuracy and interpretability. A numerical example and a case study in the retail industry are presented to demonstrate the effectiveness of the proposed approach. Results show successful customer segmentation and generation of actionable rules for marketing strategies. The research contributes to the field of customer segmentation by providing a comprehensive methodology that integrates adaptive fuzzy grid partitioning and rule generation using rough set theory. The hybrid approach offers valuable insights into customer behavior, enabling targeted marketing campaigns, personalized recommendations, and enhanced customer satisfaction.
A hybrid approach for adaptive fuzzy network partitioning and rule generation using rough set theory: Improving data-driven decision making through accurate and interpretable rules Jonhariono Sihotang; Aisyah Alesha; Juliana Batubara; Sonya Enjelina Gorat; Firta Sari Panjaitan
International Journal of Enterprise Modelling Vol. 16 No. 1 (2022): Jan: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (489.041 KB) | DOI: 10.35335/emod.v16i1.54

Abstract

Data-driven decision making is vital in credit risk assessment and other areas. Complex datasets are hard to rule. We use adaptive fuzzy network partitioning, rough set theory, and rule generation to improve data-driven credit risk assessment. An adaptive fuzzy network partitioning algorithm is used to cluster the dataset. Each cluster instance receives fuzzy membership degrees. Next, rough set-based attribute reduction identifies credit risk assessment attributes inside each cluster. Finally, attributes are used to build accurate and understandable credit risk assessment criteria. A loan application dataset is used to test the suggested method. The results show successful loan application clustering and the creation of credit risk criteria for each cluster. Accurate predictions and interpretable rules improve credit risk assessment comprehension and decision-making. By merging adaptive fuzzy network partitioning, rough set theory, and rule generation, the hybrid methodology overcomes classic technique constraints. These methods create a comprehensive framework for credit risk assessment criteria that improves accuracy and interpretability. Financial institutions and credit providers may benefit from the approach. The proposed approach can be tested in multiple domains and extended to handle increasingly complicated datasets. Evaluating the methodology on real-world datasets and comparing it to existing methods can also reveal its practicality and efficacy. This research generates accurate and interpretable rules for data-driven credit risk assessment using a hybrid method. Adaptive fuzzy network partitioning, rough set theory, and rule generation can improve decision-making across domains
Intelligent routing and scheduling strategies for heterogeneous instant delivery services: Optimizing efficiency, customer satisfaction, and sustainability Guo Wang Hou; Cen Zhou Fang
International Journal of Enterprise Modelling Vol. 16 No. 1 (2022): Jan: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (503.857 KB) | DOI: 10.35335/emod.v16i1.55

Abstract

Intelligent routing and scheduling strategies play a crucial role in optimizing efficiency, customer satisfaction, and sustainability in heterogeneous instant delivery services. This research focuses on developing a mathematical formulation and algorithm to address these challenges. The proposed model considers various factors, including delivery orders, vehicle capacities, time windows, and environmental impact, to minimize cost, delivery time, and emissions. The research also explores the integration of multi-objective optimization techniques to strike a balance between conflicting objectives. A numerical example is presented to illustrate the application of the mathematical formulation, showcasing the benefits of the proposed strategies in terms of efficient vehicle assignment, timely deliveries, and reduced environmental footprint. The findings highlight the potential for improving instant delivery services through intelligent routing and scheduling strategies, leading to enhanced operational efficiency, customer satisfaction, and sustainability. Further research is recommended to validate the proposed strategies in real-world scenarios and explore additional factors that may impact the routing and scheduling process in heterogeneous instant delivery services
Integrating machine learning and real-time optimization for heterogeneous instant delivery orders scheduling and routing Patrisia Teresa Marsoit; Patrisius Michaud Felix Marsoit; Ralp Varene
International Journal of Enterprise Modelling Vol. 16 No. 1 (2022): Jan: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (469.784 KB) | DOI: 10.35335/emod.v16i1.56

Abstract

This research aims to integrate machine learning and real-time optimization for heterogeneous instant delivery order scheduling and routing. The objective is to minimize the total delivery time while considering factors such as demand, time windows, predicted demand, and vehicle capacity constraints. By leveraging machine learning algorithms and real-time data, the proposed approach provides adaptive decision-making capabilities, allowing for dynamic adjustments in response to changing conditions. A mathematical formulation is developed to model the problem, and an algorithm is proposed to solve it. A numerical example is presented to demonstrate the effectiveness of the approach. The results highlight the optimal assignment of orders to vehicles at different time periods, leading to efficient delivery routes and minimized delivery time. The integration of machine learning and real-time optimization offers promising opportunities for enhancing the efficiency and responsiveness of delivery operations. This research contributes to advancing the field of instant delivery order scheduling and routing and paves the way for further developments in real-time logistics optimization
Efficient scheduling and routing for heterogeneous instant delivery orders: A multi-objective optimization approach with real-time adaptability Rana Kocsi Vabalas; Miloslavskaya Snyder Grillo; Wang Sheu Nguyen; Skobelev Bock
International Journal of Enterprise Modelling Vol. 16 No. 1 (2022): Jan: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (506.45 KB) | DOI: 10.35335/emod.v16i1.57

Abstract

Efficient scheduling and routing of heterogeneous instant delivery orders pose significant challenges in achieving timely and cost-effective delivery operations. In this research, we propose a multi-objective optimization approach with real-time adaptability to address these challenges. We formulate a mathematical model that considers factors such as distance, importance of orders, capacity constraints, time windows, and cost per unit distance or time. The model aims to minimize the overall cost while optimizing the assignment of delivery orders to delivery agents and determining the corresponding routes. We present a numerical example to illustrate the application of the model and discuss the results obtained. The findings highlight the effectiveness of the proposed approach in achieving efficient scheduling and routing, leading to improved resource utilization, cost reduction, and enhanced customer satisfaction. This research contributes to the field of instant delivery services by providing a systematic framework that can be employed to optimize operations in real-world delivery scenarios

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