Published: 2023-12-30
Enhancing Logistic Efficiency in Product Distribution through Genetic Algorithms (GAs) for Route Optimization
DOI: 10.35870/ijsecs.v3i3.1872
Loso Judijanto, Tribowo Rachmat Fauzan, Bobby Fisher
Abstract
This research highlights the significant potential of Genetic Algorithms (GA) as a powerful tool for optimizing logistics distribution routes. The utilization of GA has led to substantial improvements in route efficiency, resulting in cost reductions and shorter delivery times. Notably, the inclusion of customer satisfaction as a key parameter in route optimization emphasizes the importance of meeting customer expectations and ensuring timely deliveries. Additionally, the study recognizes the positive environmental implications of reduced travel distances and durations, indicating a favorable impact on environmental sustainability by reducing carbon emissions. Ethical considerations remain paramount, as the research employs anonymized data sources and adheres rigorously to industry standards to safeguard data privacy. Comparative analyses consistently favor GA over conventional distribution methods, reaffirming its capacity to generate more efficient routes. Overall, this investigation underscores the versatility and efficacy of Genetic Algorithms in addressing complex logistics distribution challenges, offering practical solutions that benefit businesses, customers, and environmental conservation alike.
Keywords
Logistic Efficiency ; Product Distribution ; Genetic Algorithm ; Route Optimization
Downloads
Article Information
This article has been peer-reviewed and published in the International Journal Software Engineering and Computer Science (IJSECS). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
-
Issue: Vol. 3 No. 3 (2023)
-
Section: Articles
-
Published: December 30, 2023
-
License: CC BY 4.0
-
Copyright: © 2023 Authors
-
DOI: 10.35870/ijsecs.v3i3.1872
AI Research Hub
This article is indexed and available through various AI-powered research tools and citation platforms. Our AI Research Hub ensures that scholarly work is discoverable, accessible, and easily integrated into the global research ecosystem. By leveraging artificial intelligence for indexing, recommendation, and citation analysis, we enhance the visibility and impact of published research.
Loso Judijanto
Public Policy Research, IPOSS Jakarta, Indonesia
Tribowo Rachmat Fauzan
Logistics Business Study Program, Faculty of Social and Political Sciences, Universitas Padjadjaran, Sumedang Regency, West Java Province, Indonesia
Bobby Fisher
Accounting Study Program, Faculty of Economics and Business, Universitas Muhammadiyah Jakarta, Central Jakarta City, Special Capital Region of Jakarta, Indonesia
-
Syafrizal, M., 2021. Web-Based SME Online Marketing System (E-Commerce). International Journal Software Engineering and Computer Science (IJSECS), 1(2), pp.75-79. DOI: https://doi.org/10.35870/ijsecs.v1i2.599.
-
Hindarto, D., 2023. The Role of E-Commerce in Increasing Sales Using Unified Modeling Language. International Journal Software Engineering and Computer Science (IJSECS), 3(2), pp.120-129. DOI: https://doi.org/10.35870/ijsecs.v3i2.1503.
-
Gen, M. and Cheng, R., 1999. Genetic algorithms and engineering optimization (Vol. 7). John Wiley & Sons.
-
Alolaiwy, M., Hawsawi, T., Zohdy, M., Kaur, A. and Louis, S., 2023. Multi-objective routing optimization in electric and flying vehicles: a genetic algorithm perspective. Applied Sciences, 13(18), p.10427.
-
Gunasekaran, A., Lai, K.H. and Cheng, T.E., 2008. Responsive supply chain: a competitive strategy in a networked economy. Omega, 36(4), pp.549-564. DOI: https://doi.org/10.1016/j.omega.2006.12.002.
-
Gunasekaran, A. and Ngai, E.W., 2005. Build-to-order supply chain management: a literature review and framework for development. Journal of operations management, 23(5), pp.423-451. DOI: https://doi.org/10.1016/j.jom.2004.10.005.
-
Mohammed, M.A., Abd Ghani, M.K., Hamed, R.I., Mostafa, S.A., Ahmad, M.S. and Ibrahim, D.A., 2017. Solving vehicle routing problem by using improved genetic algorithm for optimal solution. Journal of computational science, 21, pp.255-262. DOI: https://doi.org/10.1016/j.jocs.2017.04.003.
-
Kannan, G., Noorul Haq, A. and Devika, M., 2009. Analysis of closed loop supply chain using genetic algorithm and particle swarm optimisation. International journal of production research, 47(5), pp.1175-1200. DOI: https://doi.org/10.1080/00207540701543585.
-
Kumar, S., Jain, S. and Sharma, H., 2018. Genetic algorithms. Advances in swarm intelligence for optimizing problems in computer science, pp.27-52.
-
Renner, G. and Ekárt, A., 2003. Genetic algorithms in computer aided design. Computer-aided design, 35(8), pp.709-726. DOI: https://doi.org/10.1016/S0010-4485(03)00003-4.
-
Xin, L., Xu, P. and Manyi, G., 2022. Logistics distribution route optimization based on genetic algorithm. Computational Intelligence and Neuroscience, 2022. DOI: https://doi.org/10.1155/2022/8468438.
-
Cui, H., Qiu, J., Cao, J., Guo, M., Chen, X. and Gorbachev, S., 2023. Route optimization in township logistics distribution considering customer satisfaction based on adaptive genetic algorithm. Mathematics and Computers in Simulation, 204, pp.28-42. DOI: https://doi.org/10.1016/j.matcom.2022.05.020.
-
Yang, D. and Wu, P., 2021. E-commerce logistics path optimization based on a hybrid genetic algorithm. Complexity, 2021, pp.1-10. DOI: https://doi.org/10.1155/2021/5591811.
-
Gomes, D.E., Iglésias, M.I.D., Proença, A.P., Lima, T.M. and Gaspar, P.D., 2021. Applying a genetic algorithm to a m-TSP: case study of a decision support system for optimizing a beverage logistics vehicles routing problem. Electronics, 10(18), p.2298. DOI: https://doi.org/10.3390/electronics10182298.
-
Li, D., Cao, Q., Zuo, M. and Xu, F., 2020. Optimization of green fresh food logistics with heterogeneous fleet vehicle route problem by improved genetic algorithm. Sustainability, 12(5), p.1946. DOI: https://doi.org/10.3390/su12051946.
-
Zhang, B., 2022. The Optimization of Distribution Path of Fresh Cold Chain Logistics Based on Genetic Algorithm. Computational Intelligence and Neuroscience, 2022. DOI: https://doi.org/10.1155/2022/4667010.
-
Rui, F.U., Al-Absi, M.A., Al-Absi, A.A. and Lee, H.J., 2019, February. A Conservation Genetic Algorithm for Optimization of the E-commerce Logistics Distribution Path. In 2019 21st International Conference on Advanced Communication Technology (ICACT) (pp. 558-562). IEEE. DOI: https://doi.org/10.23919/ICACT.2019.8702053.
-
Wang, X. and Gao, J., 2022. Optimization model of logistics task allocation based on genetic algorithm. Security and Communication Networks, 2022. DOI: https://doi.org/10.1155/2022/5950876.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright and Licensing Agreement
Authors who publish with this journal agree to the following terms:
1. Copyright Retention and Open Access License
- Authors retain full copyright of their work
- Authors grant the journal right of first publication under the Creative Commons Attribution 4.0 International License (CC BY 4.0)
- This license allows unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
2. Rights Granted Under CC BY 4.0
Under this license, readers are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, including commercial use
- No additional restrictions — the licensor cannot revoke these freedoms as long as license terms are followed
3. Attribution Requirements
All uses must include:
- Proper citation of the original work
- Link to the Creative Commons license
- Indication if changes were made to the original work
- No suggestion that the licensor endorses the user or their use
4. Additional Distribution Rights
Authors may:
- Deposit the published version in institutional repositories
- Share through academic social networks
- Include in books, monographs, or other publications
- Post on personal or institutional websites
Requirement: All additional distributions must maintain the CC BY 4.0 license and proper attribution.
5. Self-Archiving and Pre-Print Sharing
Authors are encouraged to:
- Share pre-prints and post-prints online
- Deposit in subject-specific repositories (e.g., arXiv, bioRxiv)
- Engage in scholarly communication throughout the publication process
6. Open Access Commitment
This journal provides immediate open access to all content, supporting the global exchange of knowledge without financial, legal, or technical barriers.