Machine Learning based Pricing Methodology for the Logistic Domain: a Preliminary Approach

• Antonio L. Amadeu Loggi Tecnologia
• Fernando Vinturin Loggi Tecnologia
• Guilherme A. Zimeo Morais Loggi Tecnologia
• Maickel Hubner Loggi Tecnologia
• Eduardo M. Pereira Loggi Tecnologia
• Marcelo Santos Loggi Tecnologia

DOI: https://doi.org/10.5753/semish.2021.15819

Resumo

In this work, we introduce a new methodology to discover logistic regions for pricing. We use value-based characteristics from different sources, such as demographic, socioeconomic, risk, transportation, among others, to find homogeneous and valuable pricing regions. The problem was formulated as a traditional cluster solution, where well-know metrics, such as BIC and silhouette score, were used for technical validation, and business premises and constraints, operational and sales, where used to enrich feature engineering and refine cluster formation. The results presented here are from a preliminary work that was validated through several sessions with stakeholders of interest, but it is still missing the market validation. Indeed, this work will be deployed soon and a more detailed validation process, including client adherence, will be performed and monitored until the end of this year.

Palavras-chave: Geocomputação, Machine Learning, Otimização, Inteligência Computacional

Referências

Banfield, J. D. and Raftery, A. E. (1993). Model-based gaussian and non-gaussian clustering. Biometrics, pages 803–821.

Bock, H. H. (1996). Probabilistic models in cluster analysis. Computational Statistics & Data Analysis, 23(1):5–28.

Fraley, C. and Raftery, A. E. (1998). How many clusters? which clustering method? answers via model-based cluster analysis. The computer journal, 41(8):578–588.

Gentle, J. (1998). The em algorithm and extensions. Biometrics, 54(1):395.

Hartigan, J. A. (1975). Clustering algorithms. John Wiley & Sons, Inc.

Mateu, J., Lorenzo, G., and Porcu, E. (2007). Detecting features in spatial point processes with clutter via local indicators of spatial association. Journal of Computational and Graphical Statistics, 16(4):968–990.

McLachlan, G. and Chang, S. (2004). Mixture modelling for cluster analysis. Statistical methods in medical research, 13(5):347–361.

Ogbuabor, G. and Ugwoke, F. (2018). Clustering algorithm for a healthcare dataset using silhouette score value. International Journal of Computer Science & Information Technology, 10(2):27–37.

Peterson, L. E. (2009). K-nearest neighbor. Scholarpedia, 4(2):1883.

Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics, 20:53–65.