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Master's Dissertation
DOI
https://doi.org/10.11606/D.55.2021.tde-04032022-111453
Document
Author
Full name
Herlisson Maciel Bezerra
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Carlos, 2021
Supervisor
Committee
Cancho, Vicente Garibay (President)
Novelli, Cibele Maria Russo
Opazo, Miguel Angel Uribe
Suzuki, Adriano Kamimura
Title in Portuguese
Modelos de regressão espacial aplicados à previsão de demanda de pedidos de online food delivery
Keywords in Portuguese
Multilayer perceptron
Previsão de demanda
Spatial autorregressive model
Abstract in Portuguese
O crescimento do segmento de Online Food Delivery estimulou uma mudança nos hábitos de consumo de alimentos em todo mundo. A essência de uma empresa desse ramo é a operação da rede de logística para que a entrega dos pedidos seja realizada de forma rápida e confiável. Para o planejamento eficiente da logística em questão, o insumo de previsão da demanda de pedidos que são realizados em determinadas áreas geográficas é essencial. O objetivo desta dissertação é realizar a predição da demanda de pedidos por área geográfica utilizando o modelo Spatial Autorregressive Model (SAR) e o modelo de Redes Neurais Multilayer Perceptron (MLP) para os distritos na região do centro expandido de São Paulo - SP, a partir dos dados reais de uma grande empresa do segmento. Do ponto de vista metodológico, foi proposta uma abordagem com uma matriz de treino aumentada com as informações de pedidos dos vizinhos de primeira ordem para que a rede neural seja capaz de identificar a autocorrelação espacial presente nos dados, enquanto que o SAR já é estatisticamente construído para incorporar a autocorrelação espacial ao ajuste e à predição realizada. Os modelos foram ajustados com os dados reais e foram avaliados com as métricas Root Mean Squared Error (RMSE) e pelo coeficiente de determinação R2 . No resultado final, ambos os modelos tiveram desempenho satisfatório quando comparados com a média histórica dos pedidos. Na comparação entre o SAR e a MLP, a MLP com o melhor ajuste resultou em predições com o RMSE de 3,353 contra 3,604 do SAR e R2 de 0,731 contra 0,689 do SAR. Portanto, dentre os dois modelos estudados, o modelo Multilayer Perceptron foi escolhido como o melhor modelo para o ajuste aos dados analisados.
Title in English
Spatial Regression Models applied to Orders Demand Forecasting of Online Food Delivery
Keywords in English
Demand forecasting
Multilayer perceptron
Spatial autorregressive model
Abstract in English
Online Food Delivery fast-growing has estimulated changes dramatically in the habits of food consumption throughout the world. The core of any company in that business is the operation of the logitics network in order to have fast and reliable orders delivery. For planning the operation of the logistics efficiently, the demand forecasting of orders by geographical areas is crucial. The aim of this work is to predict the orders demand of geographical area of the expanded centre of São Paulo - SP - Brazil, by using the Spatial Autorregressive Model (SAR) and the Artificial Neural Network - Multilayer Perceptron Model (MLP), from real data of a Brazilian food delivery company. Methodologically, it has been proposed an approach with an augmented train matrix that incorporates orders information from the neighbourhood areas of first order so that the neural network model were able to identify the spatial autocorrelation present in the data, despite the fact that SAR model is statistically built to accout for the spatial autocorrelation to the forecast. Both of the models were trained by using real data and were evaluated with Root Mean Squared Error (RMSE) and by the coefficient of determination R2 . In the simulation, both of the models reached satisfatory performance when compared to the historical mean. In the comparison between SAR and MLP, the MLP model with the best parameters presented prediction with RMSE of 3.353 against 3.604 of the SAR, it also had R2 of 0.731 against 0.689 of the SAR. Therefore, among the two studied models, the Multilayer Perceptron has been chosen the best to fit the analysed data.
 
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Publishing Date
2022-03-04
 
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