Abstract
There is ongoing research on the problem of how to best combine predictive modeling and optimization. This is especially important in operations management, where there are complex business processes to be optimized. We propose a framework based on evolutionary computing with multi-objective particle swarm optimization and on the design of the fitness function according to the business operations to be optimized. By doing so, one can optimize a range of interesting problems using neural networks that would be otherwise hard to handle with classically supervised learning.
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References
Vanderschueren, T., Verdonck, T., Baesens, B., Verbeke, W.: Predict-then-optimize or predict-and-optimize? An empirical evaluation of cost-sensitive learning strategies. Inf. Sci. 594, 400–415 (2022)
Kraus, M., Feuerriegel, S., Oztekin, A.: Deep learning in business analytics and operations research: models, applications and managerial implications. Eur. J. Oper. Res. 281(3), 628–641 (2020)
Elmachtoub, A.N., Grigas, P.: Smart “predict, then optimize.” Manag. Sci. 68, 9–26 (2022)
Sengewald, J., Lackes, R.: Prescriptive analytics in procurement: reducing process costs. In: Wirtschaftsinformatik 2022 Proceedings (2022)
Allal-Chérif, O., Simón-Moya, V., Ballester, A.C.C.: Intelligent purchasing: how artificial intelligence can redefine the purchasing function. J. Bus. Res. 124, 69–76 (2021)
Spreitzenbarth, J.M., Bode, C., Stuckenschmidt, H.: Artificial intelligence and machine learning in purchasing and supply management: a mixed-methods review of the state-of-the-art in literature and practice. J. Purch. Supply Manag., 100896 (2024)
Guida, M., Caniato, F., Moretto, A., Ronchi, S.: The role of artificial intelligence in the procurement process: state of the art and research agenda. J. Purch. Supply Manag. 29, 100823 (2023)
Handfield, R., Jeong, S., Choi, T.: Emerging procurement technology: data analytics and cognitive analytics. IJPDLM 49, 972–1002 (2019)
Bertsimas, D., Kallus, N.: From predictive to prescriptive analytics. Manag. Sci. 66, 1025–1044 (2020)
Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., da Fonseca, V.G.: Performance assessment of multiobjective optimizers: an analysis and review. IEEE Trans. Evol. Comput. 7, 117–132 (2003)
Coello, C.C., Lechuga, M.S.: MOPSO: a proposal for multiple objective particle swarm optimization. In: Proceedings of the 2002 Congress on Evolutionary Computation. CEC 2002 (Cat. No. 02TH8600), pp. 1051–1056. IEEE (2002)
Kruse, R., Mostaghim, S., Borgelt, C., Braune, C., Steinbrecher, M.: Computational swarm intelligence. In: Kruse, R., Mostaghim, S., Borgelt, C., Braune, C., Steinbrecher, M. (eds.) Computational Intelligence. Texts in Computer Science, pp. 343–369. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-42227-1_14
Mostaghim, S., Teich, J.: Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO). In: Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS 2003 (Cat. No. 03EX706), pp. 26–33. IEEE (2003)
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Sengewald, J., Lackes, R. (2024). A Multi-objective Particle Swarm Optimization Framework for Operations Management. In: Tu, Y.P., Chi, M. (eds) E-Business. New Challenges and Opportunities for Digital-Enabled Intelligent Future. WHICEB 2024. Lecture Notes in Business Information Processing, vol 517. Springer, Cham. https://doi.org/10.1007/978-3-031-60324-2_37
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DOI: https://doi.org/10.1007/978-3-031-60324-2_37
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