DOI:
https://doi.org/10.14483/23448393.19423Published:
2024-01-13Issue:
Vol. 29 No. 1 (2024): January-AprilSection:
Computational IntelligenceMethodology for Inventory Management in Neighborhood Stores Using Machine Learning and Integer Linear Programming
Metodología para la gestión de inventario en tiendas de barrio utilizando aprendizaje de máquina y programación lineal entera
Keywords:
Machine learning, inventory, constrained optimization, demand estimation (en).Keywords:
Aprendizaje de máquina, inventario, optimización restringida, estimación de la demanda (es).Downloads
References
F. E. Castro and J. A. Oviedo, "Tiendas de barrio a la vanguardia de la competitividad," in XIX Congreso Internacional AECA, 2017, pp. 72.
E. J. Albarracín and S. C. Erazo, “Influencia de las tecnologías de la información y comunicación en el rendimiento de las micro, pequeñas y medianas empresas Colombianas,” Estudios Gerenciales, vol. 30, no. 133, pp. 355-364, 2014. https://doi.org/10.1016/j.estger.2014.06.006 DOI: https://doi.org/10.1016/j.estger.2014.06.006
J. M. Caicedo and J. M. Quiceno, “Situación actual de la tienda de barrio frente a la aparición de la nuevas superficies ARA y D1 en la ciudad de Manizales,” Undergraduate thesis, Univ. Manizales. Manizales, Caldas, Colombia. [Online]. Available: https://ridum.umanizales.edu.co/xmlui/handle/20.500.12746/2080
Semana, “El fenómeno D1: la revolución de las tiendas de descuento,” 2016. [Online]. Available: https://www.semana.com/edicion-impresa/caratula/articulo/como-funcionan-las-tiendas-d1/218767/
Portafolio, “Los tenderos no conocen a un competidor directo: la cadena D1,” 2016. [Online]. Available: https://www.portafolio.co/negocios/tenderos-conocen-competidor-directo-cadena-d1-495847
J.M. Paúcar, E. Vargas, “Propuesta de mejora de la gestión de inventarios en una empresa del sector retail,” Undergraduate thesis, Univ. Peruana Ciencias Aplicadas, Fac. Ing., Lima, Perú. [Online]. Available: http://hdl.handle.net/10757/623832
M. Ulrich and H. Jahnke, "Classification-based model selection in retail demand forecasting," Int. J. Forecast., vol. 30, no. 1, pp. 209-223, 2022. https://doi.org/10.1016/j.ijforecast.2021.05.010 DOI: https://doi.org/10.1016/j.ijforecast.2021.05.010
M. Doszyń, "Expert systems in intermittent demand forecasting," Procedia Comp. Sci., vol. 192, pp. 3598-3606, 2021. https://doi.org/10.1016/j.procs.2021.09.133 DOI: https://doi.org/10.1016/j.procs.2021.09.133
F. Tao and Q. Qi, "Data-driven smart manufacturing," J. Manuf. Syst., vol. 48, part C, pp. 157-169, 2018. https://doi.org/10.1016/j.jmsy.2018.01.006 DOI: https://doi.org/10.1016/j.jmsy.2018.01.006
J. A. Arango and J. A. Giraldo, “Gestión de compras e inventarios a partir de pronósticos Holt-Winters y diferenciación de nivel de servicio por clasificación ABC,” Scientia et Technica, vol. 18, no. 4, pp. 743-747, Dec. 2013. https://doi.org/10.22517/23447214.7171
J. E. Montemayor, Métodos de pronósticos para negocios, Monterrey, Nuevo León, México: Tecnológico de Monterrey. [Online]. Available: http://hdl.handle.net/11285/621230
T. Tanizaki and T. Hoshino, "Demand forecasting in restaurants using machine learning and statistical análisis," Procedia CIRP, vol. 79, pp. 679-683, 2019. https://doi.org/10.1016/j.procir.2019.02.042 DOI: https://doi.org/10.1016/j.procir.2019.02.042
J. P. Usuga and S. Lamouri, "Trends in machine learning applied to demand & sales forecasting: A review," presented at International Conference on Information Systems, Logistics and Supply Chain, Jul. 2018, Lyon, France. [Online]. Available: https://hal.science/hal-01881362
D. A. Collier and J. R. Evans, Administración de operaciones, Boston, MA, USA: Cengage, 2019.
T. Boone and R. Ganeshan, "Forecasting sales in the supply chain: Consumer analytics in the Big Data era," Int. J. Forecast., vol. 35, no. 1, pp. 170-180, 2019. https://doi.org/10.1016/j.ijforecast.2018.09.003 DOI: https://doi.org/10.1016/j.ijforecast.2018.09.003
A. Kumar and R. Shankar, "A Big Data driven framework for demand-driven forecasting with effects of marketing-mix variables," Ind. Marketing Manag., vol. 90, pp. 493-507, 2020. https://doi.org/10.1016/j.indmarman.2019.05.003 DOI: https://doi.org/10.1016/j.indmarman.2019.05.003
S. Chopra, "The evolution of omni-channel retailing and its impact on supply chains," Transport. Res. Procedia, vol. 30, pp. 4-13, 2018. https://doi.org/10.1016/j.trpro.2018.09.002 DOI: https://doi.org/10.1016/j.trpro.2018.09.002
S. Mou and D.J. Robb, "Retail store operations: Literature review and research directions," European J. Oper. Res., vol. 265, no. 2, pp. 399-422, 2018. https://doi.org/10.1016/j.ejor.2017.07.003 DOI: https://doi.org/10.1016/j.ejor.2017.07.003
E. Casado and M. La Civita, "Estimated time of arrival sensitivity to aircraft intent uncertainty," IFAC-PapersOnLine, vol. 51, no. 9, pp. 162-167, 2018. https://doi.org/10.1016/j.ifacol.2018.07.027 DOI: https://doi.org/10.1016/j.ifacol.2018.07.027
M. A. Sellitto and E. Balugani, "Spare parts replacement policy based on chaotic models", IFAC-PapersOnLine, vol. 51, no. 11, pp. 945-950, 2018. https://doi.org/10.1016/j.ifacol.2018.08.486 DOI: https://doi.org/10.1016/j.ifacol.2018.08.486
F. Hamilton and A. Lloyd, "Hybrid modeling and prediction of dynamical systems", PLoS Comp. Biology, vol. 13, no. 7, pp. 1-20, 2017. https://doi.org/10.1371/journal.pcbi.1005655 DOI: https://doi.org/10.1371/journal.pcbi.1005655
J. C. Sanclemente, “La reputación del tendero de barrio ante su mercado y sus consecuencias,” PhD thesis, Univ. EAFIT, Medellín, Colombia . [Online]. Available:
Servinformación, “Estudio revela que los tenderos barranquilleros madrugan más que los bogotanos”, 2019. [Online]. Available: https://www.colombia.com/actualidad/nacionales/barranquilleros-madrugan-mas-que-los-bogotanos-241787
eSemanal, “Estudio global de la cadena de suministro de retail”, 2020. [Online]. Available: https://esemanal.mx/2020/07/estudio-global-de-la-cadena-de-suministro-de-retail/
El Espectador, “Un estudio comparó las tiendas de barrio de bogotá con las de barranquilla”, 2019. [Online]. Available: https://www.elespectador.com/bogota/un-estudio-comparo-las-tiendas-de-barrio-de-bogota-con-las-de-barranquilla-article-879317/
R. Gaku, "Demand forecasting procedure for short life-cycle products with an actual food processing enterprise," Int. J. Comp. Intel. Syst., vol. 7, no. 2, pp. 85-92, 2014. https://doi.org/10.1080/18756891.2014.947121 DOI: https://doi.org/10.1080/18756891.2014.947121
J. E. Hanke and D. W. Wichern, Pronósticos en los negocios, 9th ed., Mexico DF, Mexico: Pearson Educación, 2010.
P. L. Meyer and C. P. Campos, Probabilidad y aplicaciones estadísticas, Mexico DF, Mexico: Fondo Educativo Interamericano, 1973.
M.T. Farrell, A. Correa, "Gaussian process regression models for predicting stock trends,” Relation, vol. 10, pp. 3414–3423,. https://api.semanticscholar.org/CorpusID:16646484
B. Schölkopfa and A. J. Smola, Learning with kernels: Support vector machines, regularization, optimization, and beyond, Cambridge, MA, USA: MIT press, 2002.
C. M. Bishop, Pattern recognition and machine learning (information science and statistics), 1st ed., New York, NY, USA: Springer, 2006.
K. S. Shanmugan and A. M. Breipohl, Random signals: Detection, estimation and data analysis, Hoboken, NJ, USA: Wiley, 1988.
C. E. Rasmussen, "Gaussian processes in machine learning,” in Advanced Lectures on Machine Learning, O. Bousquet, U. von Luxburg, and G. Rätsch, Eds., Berlin, Heidelberg, Germany: Springer, 2003, pp. 63-71.
C. Krzysztof and C. K. Williams, "Empirical evaluation of Gaussian Process approximation algorithms," Master's thesis, S. Informatics, Univ. Edinburgh, 2011. [Online]. Available: https://homepages.inf.ed.ac.uk/ckiw/postscript/Chalupka2011diss.pdf
How to Cite
APA
ACM
ACS
ABNT
Chicago
Harvard
IEEE
MLA
Turabian
Vancouver
Download Citation
License
Copyright (c) 2023 Carlos Alberto Henao-Baena, Bibiana Zuluaga-Zuluaga, Julian Galeano-Castro, Edward Jhohan Marín-García, Andrés Felipe Calvo-Salcedo
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
From the edition of the V23N3 of year 2018 forward, the Creative Commons License "Attribution-Non-Commercial - No Derivative Works " is changed to the following:
Attribution - Non-Commercial - Share the same: this license allows others to distribute, remix, retouch, and create from your work in a non-commercial way, as long as they give you credit and license their new creations under the same conditions.
2.jpg)
