Intelligent Techniques for Resource Allocation using Multi-Agent Systems and Multi-Criteria Methods: A Systematic Literature Review
Resumo
A alocação de recursos é um desafio presente em diversos domínios, exigindo mecanismos capazes de apoiar a tomada de decisão em ambientes complexos e dinâmicos. Neste contexto, este trabalho apresenta uma Revisão Sistemática da Literatura conduzida conforme as diretrizes PRISMA2020, com o objetivo de analisar a aplicação de Sistemas Multiagentes (MAS) e Métodos de Tomada de Decisão Multicritério (MCDM) em problemas de alocação de recursos. Foram selecionados 18 estudos relevantes, cujos resultados evidenciam a predominância de abordagens baseadas em MAS, frequentemente combinadas com métodos multicritério e técnicas de Inteligência Artificial. Os estudos reportam benefícios relacionados à adaptabilidade, descentralização e eficiência na utilização dos recursos, embora desafios de escalabilidade e aplicação em ambientes reais ainda permaneçam. Os resultados indicam que a integração entre MAS e MCDM constitui uma abordagem promissora para problemas complexos de alocação de recursos.Referências
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Aijun, L., Zhang, Y., Lu, H., Tsai, S., Hsu, C.-F., and Lee, C.-H. (2019). An innovative model to choose e-commerce suppliers. IEEE Access, PP:1–1.
Al-Shaery, A. M., Khozium, M. O., Farooqi, N. S., Alshehri, S. S., and Al-Kawa, M. A. M. (2022). Problem solving in crowd management using heuristic approach. IEEE Access, 10:25422–25434.
Alarbi, M., Jaradat, K., and Lutfiyya, H. (2023). Scope: Smart cooperative parking environment. IEEE Access, PP:1–1.
Artigues, C., Hartmann, S., and Vanhoucke, M. (2026). Fifty years of research on resource-constrained project scheduling explored from different perspectives. European Journal of Operational Research, 328(2):367–389.
Bezoui, M., Olteanu, A.-L., and Sevaux, M. (2023). Integrating preferences within multiobjective flexible job shop scheduling. European Journal of Operational Research, 305(3):1079–1086.
Cardoso, R. C. and Ferrando, A. (2021). A review of agent-based programming for multi-agent systems. Computers, 10(2).
Dahan, F. (2021). An effective multi-agent ant colony optimization algorithm for qos-aware cloud service composition. IEEE Access, 9:17196–17207.
Dominguez, R. and Cannella, S. (2020). Insights on multi-agent systems applications for supply chain management. Sustainability, 12(5).
Doostmohammadian, M., Aghasi, A., Pirani, M., Nekouei, E., Zarrabi, H., Keypour, R., Rikos, A. I., and Johansson, K. H. (2025). Survey of distributed algorithms for resource allocation over multi-agent systems. Annual Reviews in Control, 59:100983.
Dorri, A., Kanhere, S., and Jurdak, R. (2018). Multi-agent systems: A survey. IEEE Access, 6:1–1.
Gokulan, B. P. and Srinivasan, D. (2010). An introduction to multi-agent systems. Studies in Computational Intelligence, 310:1–27.
Gómez Sánchez, M., Lalla-Ruiz, E., Fernández Gil, A., Castro, C., and Voß, S. (2023). Resource-constrained multi-project scheduling problem: A survey. European Journal of Operational Research, 309(3):958–976.
Hady, M., Hu, S., Pratama, M., Cao, Z., and Kowalczyk, R. (2025a). Multi-agent reinforcement learning for resources allocation optimization: a survey. Artificial Intelligence Review, 58.
Hady, M. A., Hu, S., Pratama, M., Cao, J., and Kowalczyk, R. (2025b). Multi-agent reinforcement learning for resources allocation optimization: A survey.
Hamaali, K. and Zeebaree, S. (2021). Resources allocation for distributed systems: A review. 5:76–88.
Izmirlioglu, Y., Pham, L., Son, T. C., and Pontelli, E. (2024). A survey of multi-agent systems for smartgrids. Energies, 17(15).
Li, Y., Jiang, Y., Wu, W., Jiang, J., and Fan, H. (2019). Room allocation with capacity diversity and budget constraints. IEEE Access, PP:1–1.
Lin, T., Rivano, H., and Le Mouël, F. (2017). A survey of smart parking solutions. IEEE Transactions on Intelligent Transportation Systems, 18(12):3229–3253.
Liu, Y. and Mohamed, Y. (2008). Multi-agent resource allocation (mara) for modeling construction processes. In 2008 Winter Simulation Conference, pages 2361–2369.
Maldonado Andrade, D., Cruz, E., Abad Torres, J., Cruz, P., and Gamboa, S. (2024). Multi-agent systems: A survey about its components, framework and workflow. IEEE Access, PP:1–1.
Mamdouh, M., Ezzat, M., and Hefny, H. (2020). Airport resource allocation using machine learning techniques. Inteligencia Artificial, 23:19–32.
Mani, A. B. (2025). Técnicas de alocação e agendamento de recursos em computação em nuvem: Uma revisão abrangente. International Journal For Multidisciplinary Research.
Molina-Pariente, J. M., Hans, E. W., Framinan, J. M., and Gomez-Cia, T. (2015). New heuristics for planning operating rooms. Computers Industrial Engineering, 90:429–443.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., et al. (2021). The prisma 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 372:n71.
Rasoanaivo, R. G. and Zaraté, P. (2022). A Decision Support System for student’s room allocation. In Papathanasiou, J., Belioka, M.-P., Dikoglou, P., and Zopounidis, D., editors, ICDSST 2022 proceedings : on Decision Support addressing modern Industry, Business and Societal needs ; ISBN: 978-618-5255-18-3, pages 30–36, Thessaloniki, Greece. EWG-DSS.
Rocha, M., Silva, H., Morales, A., Sarkadi, S., and Panisson, A. (2023). Applying theory of mind to multi-agent systems: A systematic review.
Shyalika, C., Silva, T., and Karunananda, A. (2020). Reinforcement learning in dynamic task scheduling: A review. SN Computer Science, 1:306.
Singh, M. (2025). Multi-agent systems: the future of distributed ai platforms for complex task management. World Journal of Advanced Research and Reviews, 26(3):048–055.
Wooldridge, M. (2009). An Introduction to MultiAgent Systems. John Wiley & Sons, Chichester, 2 edition.
Yang, Y. (2023). Agent-Based Online Scheduling for Multi-Task Resource Allocation in Complex Environments.
Aijun, L., Zhang, Y., Lu, H., Tsai, S., Hsu, C.-F., and Lee, C.-H. (2019). An innovative model to choose e-commerce suppliers. IEEE Access, PP:1–1.
Al-Shaery, A. M., Khozium, M. O., Farooqi, N. S., Alshehri, S. S., and Al-Kawa, M. A. M. (2022). Problem solving in crowd management using heuristic approach. IEEE Access, 10:25422–25434.
Alarbi, M., Jaradat, K., and Lutfiyya, H. (2023). Scope: Smart cooperative parking environment. IEEE Access, PP:1–1.
Artigues, C., Hartmann, S., and Vanhoucke, M. (2026). Fifty years of research on resource-constrained project scheduling explored from different perspectives. European Journal of Operational Research, 328(2):367–389.
Bezoui, M., Olteanu, A.-L., and Sevaux, M. (2023). Integrating preferences within multiobjective flexible job shop scheduling. European Journal of Operational Research, 305(3):1079–1086.
Cardoso, R. C. and Ferrando, A. (2021). A review of agent-based programming for multi-agent systems. Computers, 10(2).
Dahan, F. (2021). An effective multi-agent ant colony optimization algorithm for qos-aware cloud service composition. IEEE Access, 9:17196–17207.
Dominguez, R. and Cannella, S. (2020). Insights on multi-agent systems applications for supply chain management. Sustainability, 12(5).
Doostmohammadian, M., Aghasi, A., Pirani, M., Nekouei, E., Zarrabi, H., Keypour, R., Rikos, A. I., and Johansson, K. H. (2025). Survey of distributed algorithms for resource allocation over multi-agent systems. Annual Reviews in Control, 59:100983.
Dorri, A., Kanhere, S., and Jurdak, R. (2018). Multi-agent systems: A survey. IEEE Access, 6:1–1.
Gokulan, B. P. and Srinivasan, D. (2010). An introduction to multi-agent systems. Studies in Computational Intelligence, 310:1–27.
Gómez Sánchez, M., Lalla-Ruiz, E., Fernández Gil, A., Castro, C., and Voß, S. (2023). Resource-constrained multi-project scheduling problem: A survey. European Journal of Operational Research, 309(3):958–976.
Hady, M., Hu, S., Pratama, M., Cao, Z., and Kowalczyk, R. (2025a). Multi-agent reinforcement learning for resources allocation optimization: a survey. Artificial Intelligence Review, 58.
Hady, M. A., Hu, S., Pratama, M., Cao, J., and Kowalczyk, R. (2025b). Multi-agent reinforcement learning for resources allocation optimization: A survey.
Hamaali, K. and Zeebaree, S. (2021). Resources allocation for distributed systems: A review. 5:76–88.
Izmirlioglu, Y., Pham, L., Son, T. C., and Pontelli, E. (2024). A survey of multi-agent systems for smartgrids. Energies, 17(15).
Li, Y., Jiang, Y., Wu, W., Jiang, J., and Fan, H. (2019). Room allocation with capacity diversity and budget constraints. IEEE Access, PP:1–1.
Lin, T., Rivano, H., and Le Mouël, F. (2017). A survey of smart parking solutions. IEEE Transactions on Intelligent Transportation Systems, 18(12):3229–3253.
Liu, Y. and Mohamed, Y. (2008). Multi-agent resource allocation (mara) for modeling construction processes. In 2008 Winter Simulation Conference, pages 2361–2369.
Maldonado Andrade, D., Cruz, E., Abad Torres, J., Cruz, P., and Gamboa, S. (2024). Multi-agent systems: A survey about its components, framework and workflow. IEEE Access, PP:1–1.
Mamdouh, M., Ezzat, M., and Hefny, H. (2020). Airport resource allocation using machine learning techniques. Inteligencia Artificial, 23:19–32.
Mani, A. B. (2025). Técnicas de alocação e agendamento de recursos em computação em nuvem: Uma revisão abrangente. International Journal For Multidisciplinary Research.
Molina-Pariente, J. M., Hans, E. W., Framinan, J. M., and Gomez-Cia, T. (2015). New heuristics for planning operating rooms. Computers Industrial Engineering, 90:429–443.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., et al. (2021). The prisma 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 372:n71.
Rasoanaivo, R. G. and Zaraté, P. (2022). A Decision Support System for student’s room allocation. In Papathanasiou, J., Belioka, M.-P., Dikoglou, P., and Zopounidis, D., editors, ICDSST 2022 proceedings : on Decision Support addressing modern Industry, Business and Societal needs ; ISBN: 978-618-5255-18-3, pages 30–36, Thessaloniki, Greece. EWG-DSS.
Rocha, M., Silva, H., Morales, A., Sarkadi, S., and Panisson, A. (2023). Applying theory of mind to multi-agent systems: A systematic review.
Shyalika, C., Silva, T., and Karunananda, A. (2020). Reinforcement learning in dynamic task scheduling: A review. SN Computer Science, 1:306.
Singh, M. (2025). Multi-agent systems: the future of distributed ai platforms for complex task management. World Journal of Advanced Research and Reviews, 26(3):048–055.
Wooldridge, M. (2009). An Introduction to MultiAgent Systems. John Wiley & Sons, Chichester, 2 edition.
Yang, Y. (2023). Agent-Based Online Scheduling for Multi-Task Resource Allocation in Complex Environments.
Publicado
19/10/2026
Como Citar
LOPES, Luiz Gustavo de Souza; ARAÚJO, Pedro Machado; ADAMATTI, Diana F..
Intelligent Techniques for Resource Allocation using Multi-Agent Systems and Multi-Criteria Methods: A Systematic Literature Review. In: WORKSHOP-ESCOLA DE SISTEMAS DE AGENTES, SEUS AMBIENTES E APLICAÇÕES (WESAAC), 20. , 2026, Cuiabá/MT.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 62-73.
ISSN 2326-5434.
DOI: https://doi.org/10.5753/wesaac.2026.30752.
