Inteligencia empresarial para estimular el giro comercial en el microcentro de una ciudad de tamaño intermedio

Diego Oscar Debortoli; Nélida Beatriz Brignole

doi:10.58763/rc2024195

Autores/as

Diego Oscar Debortoli Universidad Nacional del Sur. Bahía Blanca, Argentina https://orcid.org/0009-0009-4971-4403
Nélida Beatriz Brignole Universidad Nacional del Sur. Bahía Blanca, Argentina https://orcid.org/0000-0002-4795-2872

DOI:

https://doi.org/10.58763/rc2024195

Palabras clave:

comercialización, Inteligencia Artificial, Teoría de la Decisión, urbanización

Resumen

El microcentro de la ciudad de Bahía Blanca (Argentina) fue muy afectado por la pandemia y la crisis económica. La circulación está cayendo abruptamente y se ha producido el cierre definitivo de muchos comercios. En consecuencia, el objetivo final de esta investigación es disponer de una herramienta de software para toma de decisiones que permita establecer estrategias inteligentes de comercialización. El recurso informático elegido es un Sistema Inteligente para Soporte de Decisión (IDSS). En este artículo se describe el diseño conceptual de un IDSS generalizado que contribuirá a mejorar el giro comercial del microcentro bahiense. La inteligencia artificial está incluida en la recolección y análisis de datos y en un optimizador que emplea un algoritmo genético predictivo. Dentro de los aportes innovadores de este estudio, se destaca la combinación de análisis predictivos y prescriptivos como una herramienta valiosa para abordar la tarea no trivial de optimizar el giro comercial urbano. Este IDSS es capaz de evaluar y categorizar posibles escenarios hipotéticos, brindando pistas acerca de su factibilidad y conveniencia económicas. Es la primera herramienta en nuestra región destinada a la reorganización de las tiendas físicas, con miras al sostenimiento de las fuentes de trabajo del sector.

Métricas

Cargando métricas ...

Citas

Angelidou, M. (2014). Smart city policies: A spatial approach. Cities, 41, S3-S11. https://doi.org/10.1016/j.cities.2014.06.007

Arnott, D., y Pervan, G. (2014). A critical analysis of decision support systems research revisited: the rise of design science. Journal of Information Technology, 29, 269-293. https://doi.org/10.1007/978-3-319-29272-4_3

Biyeme, F., Mbakop, A., Chana, A., Voufo, J., y Meva'a, J. (2023). An analytical model for analyzing the value of information flow in the production chain model using regression algorithms and neural networks. Supply Chain Analytics, 2, 100013. https://doi.org/10.1016/j.sca.2023.100013

Bolay, J. y Kern, A. (2019) Intermediate cities. The Wiley Blackwell Encyclopedia of Urban and Regional Studies, (pp. 1–5). John Wiley & Sons Ltd.

Borisovsky, P., Eremeev, A., y Kallrath, J. (2020) Multi-product continuous plant scheduling: combination of decomposition, genetic algorithm, and constructive heuristic. International Journal of Production Research, 58(9), 2677-2695. https://doi.org/10.1080/00207543.2019.1630764

Chee, T., Chan, L., Chuah, M., Tan, C., Wong, S., y Yeoh, W. (2009). Business intelligence systems: state-of-the-art review and contemporary applications. Symposium on progress in information & communication technology, 2(4), 16-30.

Goldberg, D., y Holland J. (1988). Genetic algorithms and machine learning. Machine Learning, 3(2), 95-99. https://doi.org/10.1023/A:1022602019183

Gupta, S., Modgil, S., Bhattacharyya, S., y Bose, I. (2022). Artificial intelligence for decision support systems in the field of operations research: review and future scope of research. Annals of Operations Research, 308, 1-60. https://doi.org/10.1007/s10479-020-03856-6

Holland, J. (1975) Adaptation in natural and artificial systems. Univ of Michigan Press, MI: USA.

INDEC (2023) Censo Nacional de Población, Hogares y Viviendas 2022. Resultados provisionales. (1a ed.), Instituto Nacional de Estadística y Censos, Argentina.

Jankauskas, K., Papageorgiou, L., y Farid, S. (2019). Fast genetic algorithm approaches to solving discrete-time mixed integer linear programming problems of capacity planning and scheduling of biopharmaceutical manufacture. Computers & Chemical Engineering, 121, 212-223. https://doi.org/10.1016/j.compchemeng.2018.09.019

Jung, D., Tran, V., Quoc, D., Park, M., y Park, S. (2020). Conceptual framework of an intelligent decision support system for smart city disaster management. Applied Sciences, 10(2), 666. https://doi.org/10.3390/app10020666

Katoch, S., Chauhan, S., y Kumar, V. (2021) A review on genetic algorithm: past, present, and future. Multimedia Tools and Applications, 80, 8091–8126. https://doi.org/10.1007/s11042-020-10139-6

Keen, P., y Morton, S. (1978) Decision Support Systems: An Organizational Perspective. Addison-Wesley.

Michalewicz, Z. (1995) Do not kill unfeasible individuals. 4th Intelligent Information Systems Workshop, 110-123.

Narwadi, T., y Subiyanto, S. (2017). An application of traveling salesman problem using the improved genetic algorithm on android google maps. AIP Conference Proceedings, 1818(1). AIP Publishing.

Park, K. (2020). How CSV and CSR affect organizational performance: A productive behavior perspective. International Journal of Environmental Research and Public Health, 17(7), 2556. https://doi.org/10.3390/ijerph17072556

Petropoulos, F., Apiletti, D., Assimakopoulos, V., Babai, M., Barrow, D., Taieb, S., ..., y Ziel, F. (2022). Forecasting: theory and practice. International Journal of Forecasting, 38(3), 705-871. https://doi.org/10.1016/j.ijforecast.2021.11.001

Phillips, G. (2012). AI tools in decision making support systems: a review. International Journal on Artificial Intelligence Tools, 21(2), 1240005. https://doi.org/10.1142/S0218213012400052

Phillips, G., Mora, M., Forgionne, G., y Gupta, J. (2009). An integrative evaluation framework for intelligent decision support systems. European Journal of Operational Research, 195(3), 642-652. https://doi.org/10.1016/j.ejor.2007.11.001

Phillips, G., Daly, M., Power, D., y Adam, F. (2017). A Critical Review of Decision Support Systems Foundational Articles. 23rd Americas Conference on Information Systems, Boston.

Rahimi, I., Gandomi, A., Chen, F., y Mezura, E. (2023) Review on Constraint Handling Techniques for Population-based Algorithms: from single-objective to multi-objective optimization. Archives of Computational Methods in Engineering, 30, 2181–2209. https://doi.org/10.1007/s11831-022-09859-9

Remus, W., y Kottemann, J. (1986). Toward intelligent decision support systems: An artificially intelligent statistician. Management Information Systems Quarterly, 10(4), 403–418. http://www.jstor.org/stable/249197

Sahay, A. (2018). Business Analytics, Volume I: A Data-Driven Decision Making Approach for Business. Business Expert Press, New York.

Schewe, L., y Schmidt, M. (2019) Computing feasible points for binary MINLPs with MPECs. Mathematical Programming Computation, 11(1), 95-118. https://doi.org/10.1007/s12532-018-0141-x

Shevtshenko, E., Karaulova, T., Kramarenko, S., y Wang, Y. (2009). Manufacturing project management in the conglomerate enterprises supported by IDSS. Networks, 4, 10.

Shi, Q., Zhang, Y., Li, L., Yang, X., Li, M., y Zhou, J. (2020). SAFE: Scalable automatic feature engineering framework for industrial tasks. 36th International Conference on Data Engineering (ICDE), 1645-1656, IEEE.

Tejeda, J. (1994). Metodología para el desarrollo de DSS. [Tesis de pregrado, Instituto Tecnológico y de Estudios Superiores de Monterrey]. Repositorio Institucional.

Turban, E., Sharda, R., y Delen, D. (2011). Decision support and business intelligence systems. Prentice Hall, Pearson Upper Saddle River, New York.

Turban, E., y Watkins, P. (1986). Integrating expert systems and decision support systems. Management Information Systems Quarterly, 10(2), 121-136. https://doi.org/10.2307/249031

Wedyan, S. (2014). Review and comparison of associative classification data mining approaches. International Journal of Industrial and Manufacturing Engineering, 8(1), 34-45. https://doi.org/10.5281/zenodo.1336439