Penerapan Digital Twin dalam Simulasi dan Optimasi Proses Logistik Berkelanjutan
DOI:
https://doi.org/10.38035/jgit.v1i4.197Keywords:
Digital Twin, Simulasi Proses Logistik, Logistik BerkelanjutanAbstract
Penerapan Digital Twin dalam Simulasi dan Optimasi Proses logistik Berkelanjutan adalah artikel ilmiah studi pustaka dalam ruang lingkup teknik informatika dan logistik. Tujuan artikel ini adalah untuk membangun hipotesis bahwa Digital Twin berperan dalam meningkatkan efisiensi operasional dan keberlanjutan proses logistik. Objek riset penelitian bersumber dari pustaka online seperti Google Scholar, Mendeley, dan sumber akademik lainnya yang berfokus pada kajian teknologi Digital Twin dan optimasi logistik. Metode yang digunakan adalah library research, yang bersumber dari e-book, open access e-journal, dan literatur terkait. Analisis dilakukan secara deskriptif kualitatif. Hasil artikel ini menunjukkan bahwa:1) Digital Twin berperan dalam meningkatkan efisiensi operasional pada proses logistik; dan 2) Digital Twin berkontribusi terhadap keberlanjutan logistik melalui optimasi sumber daya dan pengurangan dampak lingkungan.
References
Abideen, A. Z., Sundram, V. P. K., Pyeman, J., Othman, A. K., & Sorooshian, S. (2021). Digital twin integrated reinforced learning in supply chain and logistics. Logistics, 5(4), 84.
Ali, H. (2024). Pengaruh Pendidikan, Informasi dan Komunikasi terhadap Internet of Things. Jurnal Manajemen Pendidikan dan Ilmu Sosial (JMPIS), 5(3).
Ali, H., & Limakrisna, N. (2013). Metodologi Penelitian (Petunjuk Praktis Untuk Pemecahan Masalah Bisnis, Penyusunan Skripsi (Doctoral dissertation, Tesis, dan Disertasi. In In Deeppublish: Yogyakarta.
Barricelli, B. R., Casiraghi, E., & Fogli, D. (2019). A Survey on Digital Twin: Definitions, Characteristics, Applications, and Design Implications. Information, 10(10), 314.
Barykin, S. Y., Bochkarev, A. A., Dobronravin, E., & Sergeev, S. M. (2021). The place and role of digital twin in supply chain management. Academy of Strategic Management Journal, 20, 1-19.
Christopher, M. (2022). Logistics and Supply Chain Management. Pearson Education.
Elgendy, N., & Elragal, A. (2014). Big data analytics: a literature review paper. In Advances in Data Mining. Applications and Theoretical Aspects: 14th Industrial Conference, ICDM 2014, St. Petersburg, Russia, July 16-20, 2014. Proceedings 14 (pp. 214-227). Springer International Publishing.
Eriana, E. S., & Zein, A. (2023). Artificial Intelligence (AI).
Grieves, M. (2014). Digital Twin: Manufacturing Excellence through Virtual Factory Replication. ResearchGate.
Higgins, O., Short, B. L., Chalup, S. K., & Wilson, R. L. (2023). Artificial intelligence (AI) and machine learning (ML) based decision support systems in mental health: An integrative review. International Journal of Mental Health Nursing, 32(4), 966-978.
Isnawati, I., & Ali, H. (2023). Determination The Internet of Things: Technological Innovation, Corporate Culture, and Computer-Based Information Systems. Siber International Journal of Digital Business (SIJDB), 1(2), 67-72.
Ivanov, D., &Dolgui, A. (2020). A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Production Planning & Control, 32(9), 775-788.
Jones, D., Snider, C., Nassehi, A., Yon, J., & Hicks, B. (2020). Characterising the Digital Twin: A systematic literature review. CIRP Journal of Manufacturing Science and Technology, 29, 36–52.
Junaidi, A. (2015). Internet of things, sejarah, teknologi dan penerapannya. Jurnal Ilmiah Teknologi Infomasi Terapan, 1(3).
Kitchenham, B., Brereton, O. P., Budgen, D., Turner, M., Bailey, J., & Linkman, S. (2009). Systematic literature reviews in software engineering–a systematic literature review. Information and software technology, 51(1), 7-15.
Kritzinger, W., Karner, M., Traar, G., Henjes, J., &Sihn, W. (2018). Digital Twin in manufacturing: A categorical literature review and classification. IFAC-PapersOnLine, 51(11), 1016-1022.
Rajaraman, V. (2016). Big data analytics. Resonance, 21, 695-716.
Schislyaeva, E. R., & Kovalenko, E. A. (2021). Innovations in logistics networks on the basis of the digital twin. Academy of Strategic Management Journal, 20, 1-17.
Shivaprakash, K. N., Swami, N., Mysorekar, S., Arora, R., Gangadharan, A., Vohra, K., ... & Kiesecker, J. M. (2022). Potential for artificial intelligence (AI) and machine learning (ML) applications in biodiversity conservation, managing forests, and related services in India. Sustainability, 14(12), 7154.
Tao, F., Zhang, M., Liu, Y., & Nee, A. Y. C. (2018). Digital Twin in Industry: State-of-the-Art. IEEE Transactions on Industrial Informatics, 15(4), 2405–2415.
Tao, F., Zhang, M., Liu, Y., & Nee, A. Y. C. (2019). Digital Twin in Industry: State-of-the-Art. IEEE Transactions on Industrial Informatics, 15(4), 2405-2415.
Vassakis, K., Petrakis, E., & Kopanakis, I. (2018). Big data analytics: applications, prospects and challenges. Mobile big data: A roadmap from models to technologies, 3-20.
Waller, M. A., & Fawcett, S. E. (2013). Data Science, Predictive Analytics, and Big Data: A Revolution that will Transform Supply Chain Design and Management. Journal of Business Logistics, 34(2), 77–84.
Wang, Y., Zhang, X., & Li, J. (2022). Application of Digital Twin Technology in Logistics and Supply Chain Management. Journal of Logistics Research, 45(2), 120-135.
Wu, W., Shen, L., Zhao, Z., Harish, A. R., Zhong, R. Y., & Huang, G. Q. (2023). Internet of everything and digital twin enabled service platform for cold chain logistics. Journal of Industrial Information Integration, 33, 100443.
Zhang, H., Li, T., & Wang, Z. (2023). Enhancing Logistics Performance through Digital Twin-Based Optimization Strategies. International Journal of Logistics Management, 34(3), 234-256.
Zhang, Z., Qu, T., Zhao, K., Zhang, K., Zhang, Y., Liu, L., ... & Huang, G. Q. (2023). Optimization model and strategy for dynamic material distribution scheduling based on digital twin: a step towards sustainable manufacturing. Sustainability, 15(23), 16539.
Zhao, Z., Zhang, M., Chen, J., Qu, T., & Huang, G. Q. (2022). Digital twin-enabled dynamic spatial-temporal knowledge graph for production logistics resource allocation. Computers & Industrial Engineering, 171, 108454.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Harri Romadhona, Zulfairah

This work is licensed under a Creative Commons Attribution 4.0 International License.
Hak cipta :
Penulis yang mempublikasikan manuskripnya di jurnal ini menyetujui ketentuan berikut:
- Hak cipta pada setiap artikel adalah milik penulis.
- Penulis mengakui bahwa Jurnal Greenation Ilmu Teknik (JGIT) berhak menjadi yang pertama menerbitkan dengan lisensi Creative Commons Attribution 4.0 International (Attribution 4.0 International CC BY 4.0) .
- Penulis dapat mengirimkan artikel secara terpisah, mengatur distribusi non-eksklusif manuskrip yang telah diterbitkan dalam jurnal ini ke versi lain (misalnya, dikirim ke repositori institusi penulis, publikasi ke dalam buku, dll.), dengan mengakui bahwa manuskrip telah diterbitkan pertama kali di JGIT.