RouteBastion: Architecting a Broker for VRP APIs
Resumo
The Vehicle Routing Problem (VRP) is a complex issue that requires advanced algorithmic problem-solving techniques and computational power. Major tech companies have developed cloud-based APIs to address this issue, offering solutions varying in cost, capabilities, and performance. However, selecting the right one for a given context is a challenging task, requiring balancing trade-offs, understanding long-term impacts, and adapting to future changes. RouteBastion, a Software as a Service (SaaS) platform, aims to unify and simplify the use of VRP-related APIs. Its modular, scalable architecture allows users to compare, select, and switch between APIs as their needs evolve. RouteBastion provides seamless integration, dynamic API selection, and decision-making, making it an intelligent middleware that enhances the flexibility and accessibility of vehicle route optimization. The paper will explore the system architecture and performance considerations to ensure RouteBastion can adapt to evolving industrial requirements.
Palavras-chave:
Problema de Roteamento de Veículos, PRV, Arquitetura de Software, Software como Serviço, Sistemas Distribuídos
Referências
Bogyrbayeva, A. et al. Machine Learning to Solve Vehicle Routing Problems: A Survey. IEEE Transactions on Intelligent Transportation Systems, v. 25, n. 6, p. 4754–4772, 2024. DOI: 10.1109/TITS.2023.3334976.
Shahin, R. et al. A survey of Flex-Route Transit problem and its link with Vehicle Routing Problem. Transportation Research Part C: Emerging Technologies, v. 158, p. 104437, 2024. ISSN 0968-090X. DOI: 10.1016/j.trc.2023.104437. Available from: [link].
Chauhan, S. S. et al. Brokering in interconnected cloud computing environments: A survey. Journal of Parallel and Distributed Computing, v. 133, p. 193–209, 2019. ISSN 0743-7315. DOI: 10.1016/j.jpdc.2018.08.001. Available from: [link].
Khorasani, N. et al. Cloud Broker: A Systematic Mapping Study. IEEE Transactions on Services Computing, v. 17, n. 5, p. 2989–3005, 2024. DOI: 10.1109/TSC.2024.3442541.
Gyani, J.; Ahmed, A.; Haq, M. A. MCDM and Various Prioritization Methods in AHP for CSS: A Comprehensive Review. IEEE Access, v. 10, p. 33492–33511, 2022. DOI: 10.1109/ACCESS.2022.3161742.
Muñoz-Villamizar, A. et al. Integration of Google Maps API with mathematical modeling for solving the Real-Time VRP. Transportation Research Procedia, v. 78, p. 32–39, 2024. 25th Euro Working Group on Transportation Meeting. ISSN 2352-1465. DOI: 10.1016/j.trpro.2024.02.005. Available from: [link].
Chowlur Revanna, J. K.; Al-Nakash, N. Y. B. Impact of ACO intelligent vehicle real-time software in finding shortest path. Software Impacts, v. 19, p. 100625, 2024. ISSN 2665-9638. DOI: 10.1016/j.simpa.2024.100625. Available from: [link].
Cavecchia, M.; Alves de Queiroz, T.; Iori, M., et al. An Optimization-Based Decision Support System for Multi-trip Vehicle Routing Problems. SN Computer Science, v. 5, p. 225, 2024. DOI: 10.1007/s42979-023-02540-3. Available from: DOI: 10.1007/s42979-023-02540-3.
Sundareswaran, S.; Squicciarini, A.; Lin, D. A Brokerage-Based Approach for Cloud Service Selection. IEEE International Conference on Cloud Computing, v. 5, 2012.
Achar, R.; Thilagam, P. S. A Broker Based Approach for Cloud Provider Selection. International Conference on Advances in Computing, Communications and Informatics (ICACCI), v. 1, 2014.
Zoie, R. C. et al. A decision making framework for weighting and ranking criteria for Cloud provider selection. International Conference on System Theory, Control and Computing (ICSTCC), v. 20, 2016.
Mukherjee, P. et al. HHO Algorithm for Cloud Service Provider Selection. In: 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE). Dec. 2020. P. 324–327. DOI: 10.1109/WIECON-ECE52138.2020.9397936.
Baranwal, G.; Vidyarthi, D. P. A framework for selection of best cloud service provider using ranked voting method. IEEE International Advance Computing Conference (IACC), v. 1, 2014.
Brown, S. The C4 model for visualising software architecture. Sept. 2024. Available from: [link]. Visited on: 5 Oct. 2024.
Kazman, R. et al. The Architecture Tradeoff Analysis Method. IEEE International Conference on Engineering of Complex Computer Systems, v. 4, 1998.
Aydemir, F.; Basçiftçi, F. Performance and Availability Analysis of API Design Techniques for API Gateways. Arabian Journal for Science and Engineering, 2024.
Shahin, R. et al. A survey of Flex-Route Transit problem and its link with Vehicle Routing Problem. Transportation Research Part C: Emerging Technologies, v. 158, p. 104437, 2024. ISSN 0968-090X. DOI: 10.1016/j.trc.2023.104437. Available from: [link].
Chauhan, S. S. et al. Brokering in interconnected cloud computing environments: A survey. Journal of Parallel and Distributed Computing, v. 133, p. 193–209, 2019. ISSN 0743-7315. DOI: 10.1016/j.jpdc.2018.08.001. Available from: [link].
Khorasani, N. et al. Cloud Broker: A Systematic Mapping Study. IEEE Transactions on Services Computing, v. 17, n. 5, p. 2989–3005, 2024. DOI: 10.1109/TSC.2024.3442541.
Gyani, J.; Ahmed, A.; Haq, M. A. MCDM and Various Prioritization Methods in AHP for CSS: A Comprehensive Review. IEEE Access, v. 10, p. 33492–33511, 2022. DOI: 10.1109/ACCESS.2022.3161742.
Muñoz-Villamizar, A. et al. Integration of Google Maps API with mathematical modeling for solving the Real-Time VRP. Transportation Research Procedia, v. 78, p. 32–39, 2024. 25th Euro Working Group on Transportation Meeting. ISSN 2352-1465. DOI: 10.1016/j.trpro.2024.02.005. Available from: [link].
Chowlur Revanna, J. K.; Al-Nakash, N. Y. B. Impact of ACO intelligent vehicle real-time software in finding shortest path. Software Impacts, v. 19, p. 100625, 2024. ISSN 2665-9638. DOI: 10.1016/j.simpa.2024.100625. Available from: [link].
Cavecchia, M.; Alves de Queiroz, T.; Iori, M., et al. An Optimization-Based Decision Support System for Multi-trip Vehicle Routing Problems. SN Computer Science, v. 5, p. 225, 2024. DOI: 10.1007/s42979-023-02540-3. Available from: DOI: 10.1007/s42979-023-02540-3.
Sundareswaran, S.; Squicciarini, A.; Lin, D. A Brokerage-Based Approach for Cloud Service Selection. IEEE International Conference on Cloud Computing, v. 5, 2012.
Achar, R.; Thilagam, P. S. A Broker Based Approach for Cloud Provider Selection. International Conference on Advances in Computing, Communications and Informatics (ICACCI), v. 1, 2014.
Zoie, R. C. et al. A decision making framework for weighting and ranking criteria for Cloud provider selection. International Conference on System Theory, Control and Computing (ICSTCC), v. 20, 2016.
Mukherjee, P. et al. HHO Algorithm for Cloud Service Provider Selection. In: 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE). Dec. 2020. P. 324–327. DOI: 10.1109/WIECON-ECE52138.2020.9397936.
Baranwal, G.; Vidyarthi, D. P. A framework for selection of best cloud service provider using ranked voting method. IEEE International Advance Computing Conference (IACC), v. 1, 2014.
Brown, S. The C4 model for visualising software architecture. Sept. 2024. Available from: [link]. Visited on: 5 Oct. 2024.
Kazman, R. et al. The Architecture Tradeoff Analysis Method. IEEE International Conference on Engineering of Complex Computer Systems, v. 4, 1998.
Aydemir, F.; Basçiftçi, F. Performance and Availability Analysis of API Design Techniques for API Gateways. Arabian Journal for Science and Engineering, 2024.
Publicado
27/11/2024
Como Citar
VIEIRA, Pietro; DALMAZO, Bruno; KREUTZ, Diego; MANSILHA, Rodrigo Brandão.
RouteBastion: Architecting a Broker for VRP APIs. In: ESCOLA REGIONAL DE REDES DE COMPUTADORES (ERRC), 21. , 2024, Rio Grande/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 7-12.
DOI: https://doi.org/10.5753/errc.2024.4560.
