cover
Article Per Year (5 Year)
All
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
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
 1 
Search results for , issue "Vol. 16 No. 2 (2022): May: Enterprise Modelling" : 5 Documents clear
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (468.826 KB) | DOI: 10.35335/emod.v16i2.58

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (482.19 KB) | DOI: 10.35335/emod.v16i2.59

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (502.611 KB) | DOI: 10.35335/emod.v16i2.60

Abstract

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

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (441.417 KB) | DOI: 10.35335/emod.v16i2.62

Abstract

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.

Page 1 of 1 | Total Record : 5

 1 

Filter by Year

2022 2022


Reset
Filter By Issues
All Issue Vol. 20 No. 1 (2026): January: Publishing Process (Inpress) Vol. 19 No. 3 (2025): September: Enterprise Modelling Vol. 19 No. 2 (2025): May: Enterprise Modelling Vol. 19 No. 1 (2025): January: Enterprise Modelling Vol. 18 No. 3 (2024): September: Enterprise Modelling Vol. 18 No. 2 (2024): May: Enterprise Modelling Vol. 18 No. 1 (2024): Jan: Enterprise Modelling Vol. 17 No. 3 (2023): September: Enterprise Modelling Vol. 17 No. 2 (2023): May: Enterprise Modelling Vol. 17 No. 1 (2023): Jan: Enterprise Modelling Vol. 16 No. 3 (2022): Sep: Enterprise Modelling Vol. 16 No. 2 (2022): May: Enterprise Modelling Vol. 16 No. 1 (2022): Jan: Enterprise Modelling Vol. 15 No. 3 (2021): Sep: Enterprise Modelling: Quantum computing Vol. 15 No. 2 (2021): May: Enterprise Modelling Vol. 15 No. 1 (2021): Jan: Enterprise Modelling Vol. 14 No. 3 (2020): Sep: Enterprise Modelling Vol. 14 No. 2 (2020): May: Enterprise Modelling Vol. 14 No. 1 (2020): Jan: Enterprise Modelling Vol. 13 No. 3 (2019): Sep: Enterprise Modelling Vol. 13 No. 2 (2019): May: Enterprise Modelling Vol. 13 No. 1 (2019): January: Fuzzy Rule Generation More Issue