An interactive analytics approach for sustainable and resilient case studies: a machine learning perspective

References

• Aghaei Pour, P., Rodemann, T., Hakanen, J., & Miettinen, K. (2022). Surrogate assisted interactive multiobjective optimization in energy system design of buildings. Optimization and Engineering, 23(1), 303–327. https://doi.org/10.1007/s11081-020-09587-8
   Web of Science ®Google Scholar
• Agushaka, J. O., Ezugwu, A. E., & Abualigah, L. (2022). Dwarf mongoose optimization algorithm. Computer Methods in Applied Mechanics and Engineering, 391, 114570. https://doi.org/10.1016/j.cma.2022.114570
   Web of Science ®Google Scholar
• Ash, C., Diallo, C., Venkatadri, U., & VanBerkel, P. (2022). Distributionally robust optimization of a Canadian healthcare supply chain to enhance resilience during the COVID-19 pandemic. Computers & Industrial Engineering, 168, 108051. https://doi.org/10.1016/j.cie.2022.108051
   PubMed Web of Science ®Google Scholar
• Buba, A. T., & Lee, L. S. (2018). A differential evolution for simultaneous transit network design and frequency setting problem. Expert Systems with Applications, 106, 277–289. https://doi.org/10.1016/j.eswa.2018.04.011
   Web of Science ®Google Scholar
• Buba, A. T., & Lee, L. S. (2019). Hybrid differential evolution-particle swarm optimization algorithm for multiobjective urban transit network design problem with homogeneous buses. Mathematical Problems in Engineering, 2019, 1–16. https://doi.org/10.1155/2019/5963240
   Web of Science ®Google Scholar
• Cheng, R., Jin, Y., Olhofer, M., & Sendhoff, B. (2016). A reference vector guided evolutionary algorithm for many-objective optimization. IEEE Transactions on Evolutionary Computation, 20(5), 773–791. https://doi.org/10.1109/TEVC.2016.2519378
   Web of Science ®Google Scholar
• Corominas, G. R., Blesa, M. J., & Blum, C. (2023). AntNetAlign: Ant colony optimization for network alignment. Applied Soft Computing, 132, 109832. https://doi.org/10.1016/j.asoc.2022.109832
   Google Scholar
• Cui, Y., Meng, X., & Qiao, J. (2022). A multiobjective particle swarm optimization algorithm based on two-archive mechanism. Applied Soft Computing, 119, 108532. https://doi.org/10.1016/j.asoc.2022.108532
   Web of Science ®Google Scholar
• Deb, K. (2001). Multiobjective optimization using evolutionary algorithms (1st ed.). Wiley.
   Google Scholar
• Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. (2000). A fast elitist non-dominated sorting genetic algorithm for multiobjective optimization: NSGA-II. In Schoenauer, M., Deb, K., Rudolph, G., Yao, X., Lutton, E., Merelo, J. J. H.-P. Schwefel. Ed. Parallel problem solving from nature PPSN 1917 Vol. VI pp. 849–858. Springer. https://doi.org/10.1007/3-540-45356-3_83
   Google Scholar
• Deb, K., & Jain, H. (2014). An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: Solving problems with box constraints. IEEE Transactions on Evolutionary Computation, 18(4), 577–601. https://doi.org/10.1109/TEVC.2013.2281535
   Web of Science ®Google Scholar
• Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197. https://doi.org/10.1109/4235.996017
   Web of Science ®Google Scholar
• Deb, K., & Sundar, J. (2006). Reference point based multiobjective optimization using evolutionary algorithms. Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation - GECCO ’06, (pp. 635). https://doi.org/10.1145/1143997.1144112
   Google Scholar
• Faramarzi-Oghani, S., Dolati Neghabadi, P., Talbi, E. -G., & Tavakkoli-Moghaddam, R. (2022). Meta-heuristics for sustainable supply chain management: A review. International Journal of Production Research, 61(6), 1–31. https://doi.org/10.1080/00207543.2022.2045377
   Web of Science ®Google Scholar
• Fiedler, A. (2022). An agent-based negotiation protocol for supply chain finance. Computers & Industrial Engineering, 168, 108136. https://doi.org/10.1016/j.cie.2022.108136
   Web of Science ®Google Scholar
• Gunantara, N., & Ai, Q. (2018). A review of multiobjective optimization: Methods and its applications. Cogent Engineering, 5(1), 1502242. https://doi.org/10.1080/23311916.2018.1502242
   Web of Science ®Google Scholar
• Hakanen, J., Chugh, T., Sindhya, K., Jin, Y., & Miettinen, K. (2016). Connections of reference vectors and different types of preference information in interactive multiobjective evolutionary algorithms. 2016 IEEE Symposium Series on Computational Intelligence (SSCI), (pp. 1–8). https://doi.org/10.1109/SSCI.2016.7850220
   Google Scholar
• Liao, Y. -C., Dudley, J. J., Mo, G. B., Cheng, C. -L., Chan, L., Oulasvirta, A., & Kristensson, P. O. (2023). Interaction design with multiobjective bayesian optimization. IEEE Pervasive Computing. https://doi.org/10.1109/MPRV.2022.3230597
   Google Scholar
• Liu, S., Lu, Y., Li, J., Shen, X., Sun, X., & Bao, J. (2023). A blockchain-based interactive approach between digital twin-based manufacturing systems. Computers & Industrial Engineering, 175, 108827. https://doi.org/10.1016/j.cie.2022.108827
   Web of Science ®Google Scholar
• Mandl, C. E. (1980). Evaluation and optimization of urban public transportation networks. European Journal of Operational Research, 5(6), 396–404. https://doi.org/10.1016/0377-2217(80)90126-5
   Web of Science ®Google Scholar
• Miettinen, K. (2012). Nonlinear multiobjective optimization. Springer Science & Business Media.
   Google Scholar
• Miettinen, K., Eskelinen, P., Ruiz, F., & Luque, M. (2010). NAUTILUS method: An interactive technique in multiobjective optimization based on the nadir point. European Journal of Operational Research, 206(2), 426–434. https://doi.org/10.1016/j.ejor.2010.02.041
   Web of Science ®Google Scholar
• Miettinen, K., Ruiz, F., & Wierzbicki, A. P. (2008). Introduction to multiobjective optimization: Interactive approaches. In J. Branke, K. Deb, K. Miettinen, & R. Słowiński (Eds.), Multiobjective optimization (Vol. 5252, pp. 27–57). Springer. https://doi.org/10.1007/978-3-540-88908-3_2
   Google Scholar
• Mousavi, S. M., Alikar, N., Niaki, S. T. A., & Bahreininejad, A. (2015). Two tuned multiobjective meta-heuristic algorithms for solving a fuzzy multi-state redundancy allocation problem under discount strategies. Applied Mathematical Modelling, 39(22), 6968–6989. https://doi.org/10.1016/j.apm.2015.02.040
   Web of Science ®Google Scholar
• Moya, I., Chica, M., & Cordón, Ó. (2019). A multicriteria integral framework for agent-based model calibration using evolutionary multiobjective optimization and network-based visualization. Decision Support Systems, 124, 113111. https://doi.org/10.1016/j.dss.2019.113111
   Web of Science ®Google Scholar
• Narula, S. C., & Weistroffer, H. R. (1989). A flexible method for nonlinear multicriteria decision-making problems. IEEE Transactions on Systems, Man, and Cybernetics, 19(4), 883–887. https://doi.org/10.1109/21.35354
   Web of Science ®Google Scholar
• Ojalehto, V., Podkopaev, D., & Miettinen, K. (2015). Agent assisted interactive algorithm for computationally demanding multiobjective optimization problems. Computers & Chemical Engineering, 77, 105–115. https://doi.org/10.1016/j.compchemeng.2015.03.004
   Web of Science ®Google Scholar
• Petchrompo, S., Coit, D. W., Brintrup, A., Wannakrairot, A., & Parlikad, A. K. (2022). A review of Pareto pruning methods for multiobjective optimization. Computers & Industrial Engineering, 167, 108022. https://doi.org/10.1016/j.cie.2022.108022
   Web of Science ®Google Scholar
• Ruiz, A. B., Saborido, R., & Luque, M. (2015). A preference-based evolutionary algorithm for multiobjective optimization: The weighting achievement scalarizing function genetic algorithm. Journal of Global Optimization, 62(1), 101–129. https://doi.org/10.1007/s10898-014-0214-y
   Web of Science ®Google Scholar
• Ruiz, A. B., Sindhya, K., Miettinen, K., Ruiz, F., & Luque, M. (2015). E-NAUTILUS: A decision support system for complex multiobjective optimization problems based on the NAUTILUS method. European Journal of Operational Research, 246(1), 218–231. https://doi.org/10.1016/j.ejor.2015.04.027
   Web of Science ®Google Scholar
• Saini, B. S., Emmerich, M., Mazumdar, A., Afsar, B., Shavazipour, B., & Miettinen, K. (2022). Optimistic Nautilus navigator for multiobjective optimization with costly function evaluations. Journal of Global Optimization, 83(4), 1–25. https://doi.org/10.1007/s10898-021-01119-7
   Web of Science ®Google Scholar
• Siwik, L., & Natanek, S. (2008). Elitist evolutionary multi-agent system in solving noisy multiobjective optimization problems. 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), (pp. 3319–3326). https://doi.org/10.1109/CEC.2008.4631247
   Google Scholar
• Smedberg, H., & Bandaru, S. (2023). Interactive knowledge discovery and knowledge visualization for decision support in multiobjective optimization. European Journal of Operational Research, 306(3), 1311–1329. https://doi.org/10.1016/j.ejor.2022.09.008
   Web of Science ®Google Scholar
• Ulusam Seçkiner, S., & Koç, A. (2022). Agent-based simulation and simulation optimization approaches to energy planning under different scenarios: A hospital application case. Computers & Industrial Engineering, 169, 108163. https://doi.org/10.1016/j.cie.2022.108163
   Web of Science ®Google Scholar
• Vasile, M., & Zuiani, F. (2011). Multi-agent collaborative search: An agent-based memetic multiobjective optimization algorithm applied to space trajectory design. Proceedings of the Institution of Mechanical Engineers: Part G, Journal of Aerospace Engineering, 225(11), 1211–1227. https://doi.org/10.1177/0954410011410274
   Google Scholar
• Wierzbicki, A. P. (1980). The use of reference objectives in multiobjective optimizationMultiple Criteria Decision Making Theory and Application. G. Fandel & T. Gal Eds.Vol. 177. Springer. https://doi.org/10.1007/978-3-642-48782-8_32
   Google Scholar
• Xin, B., Chen, L., Chen, J., Ishibuchi, H., Hirota, K., & Liu, B. (2018). Interactive multiobjective optimization: A review of the state-of-the-art. IEEE Access, 6, 41256–41279. https://doi.org/10.1109/ACCESS.2018.2856832
   Web of Science ®Google Scholar
• Zhang, Z., Wu, L., Zhang, W., Peng, T., & Zheng, J. (2021). Energy-efficient path planning for a single-load automated guided vehicle in a manufacturing workshop. Computers & Industrial Engineering, 158, 107397. https://doi.org/10.1016/j.cie.2021.107397
   Web of Science ®Google Scholar