Evaluasi Metode Penugasan Maksimum Dalam Optimal Penempatan Sumber Daya Melalui Pendekatan Systematis Literature Review
Keywords:
Maximum Assignment Method; Optimal Resource Allocation; Resource Placement ApproachAbstract
Penelitian ini bertujuan untuk mengevaluasi berbagai metode, termasuk Metode Tugas Maksimal, Metode Alokasi Sumber Daya Optimal, dan Metode Penempatan Sumber Daya, untuk menemukan metode yang paling efektif untuk penempatan sumber daya optimal. Pendekatan Sistematic Literature Review (SLR), yang didasarkan pada pedoman PRISMA 2020, digunakan dalam penelitian ini. Sumber literatur dalam penelitian ini berasal dari basis data Scopus pada jurnal bereputasi dari kuartil pertama hingga kuartil keempat tahun 2020–2025. Dari 424 artikel yang dikirim, 16 terpilih karena relevan dan berkualitas.Hasil analisis menunjukkan bahwa metode berbasis optimasi multi-objektif dan kecerdasan buatan (AI) seperti Artificial Bee Colony (ABC), Optimization of Particle Swarms (PSO), Multi-Objective Cuckoo Search (MOCS), Reinforcement Learning, dan Deep Learning dapat meningkatkan efisiensi sistem hingga 70%, menurunkan biaya operasional hingga 30%, dan mengurangi emisi karbon hingga 40%. Diharapkan bahwa penelitian ini akan memberikan landasan ilmiah untuk membangun model manajemen sumber daya kontemporer yang efektif, fleksibel, dan berkelanjutan. Selain itu, akan memberikan arah bagi penelitian lebih lanjut yang berkaitan dengan penerapan metode penugasan maksimum dalam dunia nyata.
References
Alqahtani, M., & Alghamdi, A. S. (2025). Optimized Coordination of Distributed Energy Resources in Modern Distribution Networks Using a Hybrid Metaheuristic Approach.
Babar, M., & R-moreno, M. D. (2024). An integrated model with interdependent water storage for optimal resource management in Energy – Water – Food Nexus. Journal of Cleaner Production, 462(May), 142648. https://doi.org/10.1016/j.jclepro.2024.142648
Challoumis, C. (2024). Integrating Money Cycle Dynamics and Economocracy for Optimal Resource Allocation and Economic Stability.
Davoodi, M., Kaleybar, H. J., Brenna, M., & Zaninelli, D. (2025). Energy Resources : Optimal Operation and Smart Energy Management System. 2025. https://doi.org/10.1155/etep/6626245
Dehghani, M., & Bornapour, S. M. (2025). Heliyon Smart homes energy management : Optimal multi-objective appliance scheduling model considering electrical energy storage and renewable energy resources. Heliyon, 11(3), e42417. https://doi.org/10.1016/j.heliyon.2025.e42417
Eldeeb, H. B., Hosney, M., Elsayed, H. M., & Badr, R. I. (2020). Optimal Resource Allocation and Interference Management for Multi-User Uplink Light Communication Systems With Angular Diversity Technology. 8. https://doi.org/10.1109/ACCESS.2020.3036616
Eryiğit, M., Oleiwi, S., Baker, A., & Najm, A. (2023). Optimal management of multiple water resources by a heuristic optimization for a water supply in the desert cities of Western Iraq. Desalination and Water Treatment, 281, 7–14. https://doi.org/10.5004/dwt.2023.28239
Fei, L., Shahzad, M., Abbas, F., Muqeet, H. A., Hussain, M. M., & Bin, L. (2022). Optimal Energy Management System of IoT-Enabled Large Building Considering Electric Vehicle Scheduling , Distributed Resources , and Demand Response Schemes.
Ferraz, R. S. F., Ferraz, R. S. F., & Rueda-medina, A. C. (2025). Multi-Objective Approach for Distribution System Planning Considering Stochastic Customer-Owned Distributed Energy Resources. March. https://doi.org/10.1109/ACCESS.2025.3547277
Hamadneh, T., Alsayyed, O., Batiha, B., Barhoumi, E. M., Kamarposhti, M. A., Haghighi, H., & Thounthong, P. (2025). Optimal energy management of distributed generation resources in a microgrid under various load and solar irradiance conditions using the artificial bee colony algorithm. 1–21.
Kim, E., Member, S., Choi, H., & Member, S. (2022). Optimal Resource Allocation Considering Non-Uniform Spatial Traffic Distribution in Ultra-Dense Networks : A Multi-Agent Reinforcement Learning Approach. IEEE Access, 10, 20455–20464. https://doi.org/10.1109/ACCESS.2022.3152162
Kumar, K. S., Alzubi, J. A., Member, S., Kandasamy, V., & Ali, G. (2024). A Secure and Efficient BlockChain and Distributed Ledger Technology-Based Optimal Resource Management in Digital Twin Beyond 5G Networks Using Hybrid Energy Valley and Levy Flight Distributer Optimization Algorithm. IEEE Access, 12(July), 110331–110352. https://doi.org/10.1109/ACCESS.2024.3435847
Li, K., Zheng, D., Hajibabai, L., & Hajbabaie, A. (2025). A relaxation-based Voronoi diagram approach for equitable resource distribution. January 2024, 445–463. https://doi.org/10.1111/mice.13339
Mallick, U. K. (2023). Mathematical Modeling for Optimal Management of Human Resources in Banking Sector of Bangladesh. 2023. https://doi.org/10.1155/2023/1321365
Maria, E., Baybusinov, I. B., Zhao, J., & Zhou, L. (2021). The Stable Marriage Problem : An interdisciplinary review from the physicist ’ s perspective. Physics Reports, 917, 1–79. https://doi.org/10.1016/j.physrep.2021.03.001
Osorio, D. M., & Garcia, J. R. (2023). Distributed Energy Resources in AC Distribution Networks Distribution Function.
Page, M. J., Mckenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-wilson, E., Mcdonald, S., … Moher, D. (2021). The PRISMA 2020 statement : an updated guideline for reporting systematic reviews Systematic reviews and Meta-Analyses. https://doi.org/10.1136/bmj.n71
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. Bmj, 372. https://doi.org/10.1136/bmj.n71
Radha, M., Ananthalakshmi, S., & Parameswari, R. U. (2020). A modern approach for solving interval based assignment problem. 9(5), 2541–2545.
Rjeib, H. D., & Kecskemeti, G. (2024). VMP-ER : An Efficient Virtual Machine Placement Algorithm for Energy and Resources Optimization in Cloud Data Center. 1–14.
Zhang, Y., Liu, W., Liang, C., Wang, H., Zhang, J., & Zeng, B. (2024). A multi-objective interval optimization approach to expansion planning of active distribution system with distributed internet data centers and renewable energy resources. July, 2999–3016. https://doi.org/10.1049/gtd2.13249
Zhou, T., Laghrouche, S., & Ait-amirat, Y. (2025). Distributed time-varying optimal resource management for microgrids via a fixed-time multiagent approach. 1–24.
