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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 121 Documents

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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

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

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

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

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

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

Machine learning-based multi-objective optimization for dynamic scheduling and routing of heterogeneous instant delivery orders and scheduling strategies with real-time adaptation Ramson Rikson Maruwahal Sijabat; Zhou Klapp Parodos
International Journal of Enterprise Modelling Vol. 16 No. 2 (2022): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

This research develops a machine learning-based multi-objective optimization technique for dynamic scheduling and routing heterogeneous instant delivery orders. Instant delivery service providers confront issues improving their operations due to order characteristics, time windows, vehicle capabilities, and real-time adaption. Scheduling, routing, and optimization literature for immediate delivery services is reviewed to start the investigation. Based on gaps, a new mathematical formulation is proposed to model the problem. Machine learning allows adaptive and dynamic decision-making. The formulation is used to address the optimization problem utilizing a method. Machine learning algorithms use past data to anticipate, optimize, and schedule routes. Real-time adaption solutions address changing order characteristics and operating situations. Numerical examples and case studies evaluate the proposed approach. The optimization approach solves difficult scheduling and routing problems in these cases. The research improves operational efficiency, cost savings, and order satisfaction. This research introduces a machine learning-based multi-objective optimization framework for rapid delivery order scheduling and routing. The findings help immediate delivery service providers streamline operations, boost customer happiness, and maximize resource use. To create more comprehensive optimization models, future research can integrate traffic circumstances, environmental implications, and customer preferences

Algorithmic innovations and robust solutions for time windows and stochastic demands in vehicle routing Desrosiers Goel Zarouk; Chung Wang Xu; Erten Wang Cacchiani
International Journal of Enterprise Modelling Vol. 16 No. 2 (2022): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

This research addresses time windows and stochastic demands in vehicle routing using algorithmic improvements and robust solutions. Optimizing delivery operations requires managing routes and schedules while considering demand uncertainty and severe time frame limits. The research starts with a mathematical formulation that includes consumer locations, stochastic demands, time windows, and costs. Algorithms are added to handle uncertain requests and severe time window restrictions. Demand forecasting, route optimization, and uncertainty-based decision-making are used in the suggested strategy. The proposed routing method models stochastic requests using historical demand data and probability distributions. To create effective delivery plans, it analyzes client visit sequencing, vehicle capabilities, and time window limits. Numerical examples and case studies validate the proposed approach. Numerical examples show how the mathematical theory and algorithm address vehicle routing issues with time windows and stochastic demands. Case studies demonstrate how algorithmic advances and robust solutions benefit logistics firms in real-world circumstances. The proposed approach improves efficiency, cost savings, and customer satisfaction. Optimized routes and timetables help handle uncertain demand patterns, resource use, and time slots. Discussing the solutions' scalability and adaptability sheds light on their application and future research. This research provides algorithmic breakthroughs and robust solutions for vehicle routing time windows and stochastic needs. Logistics companies can increase operational efficiency and customer service with the findings. The proposed method optimizes delivery operations under uncertainty and time restrictions, helping logistics organizations compete in a changing business environment.

Tackling uncertainty in vehicle routing: Advancements in time windows and stochastic demands optimization Fristi Riandari; Demita Sihotang; Hamed Huckle Schubert
International Journal of Enterprise Modelling Vol. 16 No. 2 (2022): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

This research focuses ons addresses vehicle routing uncertainty in time windows and stochastic needs. The project intends to increase vehicle routing efficiency, adaptability, and robustness by developing optimization approaches. Traffic congestion, unanticipated events, and changing client expectations can greatly impact truck routing solutions. Traditional methods presume fixed time frames and deterministic needs, resulting in suboptimal or infeasible paths. This paper presents a mathematical model that includes time window uncertainty and stochastic needs into the vehicle routing issue to address these restrictions. The formulation incorporates arrival times, delivery amounts, and route decisions to minimize transportation costs and ensure timely deliveries and resource efficiency. Advanced algorithms and solvers tackle the optimization challenge. Integer programming, flow conservation constraints, and temporal window constraints are used to identify optimal or near-optimal solutions to uncertainty and dynamic changes. Numerical examples and case studies demonstrate the approach's efficacy. Numerical examples demonstrate the mathematical formulation, while the case study shows the practical consequences and benefits for a dynamic delivery service organization. The research shows that the proposed approach can handle temporal window uncertainties and stochastic demands. These innovations can optimize vehicle routing, reduce transportation costs, boost customer happiness, and increase resource utilization. Addressing time window uncertainty and stochastic demands advances vehicle routing. The proposed approach helps logistics and transportation industries overcome dynamic and uncertain operating environments, boosting operational efficiency and competitiveness.

Robust routing optimization for vehicle routing problem with stochastic demands and time windows: Considering uncertainty and time constraints in logistics planning Nunez Ruiz; Zofio Barbero
International Journal of Enterprise Modelling Vol. 16 No. 2 (2022): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/emod.v16i2.61

This research addresses the challenge of robust routing optimization in the context of the Vehicle Routing Problem (VRP) with stochastic demands and time windows. The objective is to develop an effective logistics planning approach that considers demand uncertainty and time constraints in order to minimize costs and improve operational efficiency. A mathematical formulation is presented to model the problem, considering a robustness parameter to account for uncertainty in demand scenarios. The formulation incorporates binary decision variables to determine the routing plan and meet customer demands within specified time windows. A numerical example is provided to illustrate the application of the model, highlighting the impact of uncertainty and time window compliance on the routing plan and total expected cost. The results demonstrate the potential benefits of employing robust routing optimization, providing insights for logistics planners and decision-makers in designing more resilient and cost-effective routing strategies. Further research can explore advanced algorithms and real-world case studies to validate and enhance the proposed approach in practical logistics scenarios

Optimizing robust routing and production planning in stochastic supply chains: Addressing uncertainty of timing and demand for enhanced resilience and efficiency Kelle Snyder Han; Kouvelis Geovany Ortizan
International Journal of Enterprise Modelling Vol. 16 No. 2 (2022): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

Unpredictable timing and demand changes can greatly impair supply chain performance and resilience. Optimizing robust routing and production planning in stochastic supply chains improves efficiency and adaptability. Addressing timing and demand uncertainty improves resilience and efficiency. Supply chain management research emphasizes stochastic factors and resilient optimization. This research introduces a mathematical model that accounts for stochastic demand, transportation costs, holding costs, production capabilities, and lead times. The formulation minimizes cost while meeting uncertain demand and capacity constraints. Numerical examples demonstrate the model's use. Due to restrictions, the numerical example results are not supplied, but expected outputs include optimal routing and production plans, total cost minimization, sensitivity analysis, and insights into uncertainty. Comparisons with baseline situations can show how the proposed strategy improves resilience and efficiency. Supply chains may become more resilient, flexible, and efficient by optimizing routing and production planning in uncertainty. This research introduces stochastic components and resilient optimization methods to supply chain management. To improve the proposed approach in real-world supply chains, further research can examine improved algorithms, real-time data integration, and practical implementation strategies.

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Zeehimin Huang Ping

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internationalenterpriseintegra@gmail.com

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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

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Abstract

Abstract

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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