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Rodríguez Aguilar, Román
Selecting the Distribution System using AHP and Fuzzy AHP Methods
Demand prediction using a soft-computing approach : a case study of automotive industry
2024
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Saucedo-Martínez, Jania Astrid
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Salais-Fierro, Tomás Eloy
,
Rodríguez Aguilar, Román
,
Marmolejo Saucedo, José Antonio
In this research, we present a supporting tool for decision making by designing a distribution system for a trading company of supplies for the welding industry in Mexico. The case study encompasses a distribution system with shortage problems and poor fleet capacity. To address these problems, improvement options were grouped into three possible scenarios through a third-party logistics (3PL) service. Furthermore, for the evaluation and selection of one of the scenarios, the Analytic Hierarchy Process (AHP) methodology was proposed integrating fuzzy logic as a tool for decision making, including factors of uncertainty and subjectivity as well as a comparison with traditional AHP obtaining the best scenario, meeting the requirements of the company, and showing potential improvements in the desired service level for its distribution system. © 2024 Springer Nature
2020
,
Salais-Fierro, Tomás Eloy
,
Saucedo-Martínez, Jania
,
Rodríguez Aguilar, Román
,
Vela-Haro, Jose Manuel
According to the literature review performed, there are few methods focused on the study of qualitative and quantitative variables when making demand projections by using fuzzy logic and artificial neural networks. The purpose of this research is to build a hybrid method for integrating demand forecasts generated from expert judgements and historical data and application in the automotive industry. Demand forecasts through the integration of variables; expert judgements and historical data using fuzzy logic and neural network. The methodology includes the integration of expert and historical data applying the Delphi method as a means of collecting fuzzy date. The result according to proposed methodology shows how fuzzy logic and neural networks is an alternative for demand planning activity. Machine learning techniques are techniques that generate alternatives for the tools development for demand forecasting. In this study, qualitative and quantitative variables are integrated through the implementation of fuzzy logic and time series artificial neural networks. The study aims to focus in manufacturing industry factors in conjunction time series data. © 2019 by the authors.