Created by W.Langdon from gp-bibliography.bib Revision:1.7546
As part of product development activities, the demand for products is usually forecasted to prevent revenue loss. However, most of these forecasts require large amount of historical data to develop a demand forecast model. With the advent of the internet, manufacturers can integrate constantly updated user generated online data in forecasting models in order to forecast the adoption of products. To overcome the above limitations, the objectives of this research are presented in three phases: i) To propose a novel customers satisfaction model that address the fuzziness and nonlinearity of customer satisfaction models using multigene genetic programming based fuzzy regression (MGGP-FR) ii) To formulate a methodology for determining and predicting the importance of product attributes. The Shapely Value and Choquet integral are employed to estimate the importance of product attributes and based on the importance values, a fuzzy rough set times series method is proposed to forecast the future importance of product attributes. iii) To propose a new market share model and demand forecasting model that addresses uncertainties in forecasting. A market share model is developed from the multinomial logit (MNL) model and the fuzzy regression (FR) approach while the demand model is developed from a modified Bass model integrated with sentiment scores from online reviews.
A case study on modeling customer satisfaction for electronic hairdryers using MGGP-FR is presented in this study. To validate the proposed methodology, the results of the MGGP-FR are compared with previously proposed methods mainly FR, genetic programming (GP), and genetic programming-based fuzzy regression (GP-FR). Based on the mean relative errors and the variance of errors of the MGGP-FR and previous methods, the proposed MGGP-FR showed a better performance when compared with the previous methods. Next, forecasting the future importance of the product attributes of an electronic hairdryer is illustrated using the fuzzy rough set time series method. The proposed fuzzy rough set time series forecasting accuracy outperformed the fuzzy time series method. Lastly, a case study on forecasting the adoption of a Tablet P.C is used to illustrate the applicability of the proposed fuzzy modelling and discrete choice analysis method for forecasting product adoption using online reviews. The proposed method was compared with the fuzzy time series forecasting and the original Bass model and was found to be better as it provided different scenarios for the forecast and acceptable forecasting results.",
Genetic Programming entries for Hanan Yakubu