In this research, neural network (NN) and genetic algorithm (GA) are used together to design optimal NN structure. The proposed approach combines the characteristics of GA and NN…
Abstract
In this research, neural network (NN) and genetic algorithm (GA) are used together to design optimal NN structure. The proposed approach combines the characteristics of GA and NN to reduce the computational complexity of artificial intelligence applications in design and manufacturing. Genetic input selection approach is introduced to obtain optimal NN topology. Experimental results are given to evaluate the performance of the proposed system.
Details
Keywords
Asli Aksoy, Nursel Ozturk and Eric Sucky
Demand forecasting in the clothing industry is very complex due to the existence of a wide range of product references and the lack of historical sales data. To the authors'…
Abstract
Purpose
Demand forecasting in the clothing industry is very complex due to the existence of a wide range of product references and the lack of historical sales data. To the authors' knowledge, there is an inadequate number of literature studies to forecast the demand with the adaptive network based fuzzy inference system for the clothing industry. The purpose of this paper is to construct a decision support system for demand forecasting in the clothing industry.
Design/methodology/approach
The adaptive‐network‐based fuzzy inference system (ANFIS) is used for forecasting demand in the clothing industry.
Findings
The results of the proposed study showed that an ANFIS‐based demand forecasting system can help clothing manufacturers to forecast demand more accurately, effectively and simply.
Originality/value
In this study, the demand is forecast in terms of clothing manufacturers by using ANFIS. ANFIS is a new technique for demand forecasting, it combines the learning capability of the neural networks and the generalization capability of the fuzzy logic. The input and output criteria are determined based on clothing manufacturers' requirements and via literature research, and the forecasting horizon is about one month. The study includes the real life application of the proposed system and the proposed system is tested by using real demand values for clothing manufacturers.
Details
Keywords
Asli Aksoy, Nursel Öztürk and Eric Sucky
According to literature research and conversations with apparel manufacturers' specialists, there is not any common analytic method for demand forecasting in apparel industry and…
Abstract
Purpose
According to literature research and conversations with apparel manufacturers' specialists, there is not any common analytic method for demand forecasting in apparel industry and to the authors' knowledge, there is not adequate number of study in literature to forecast the demand with adaptive network-based fuzzy inference system (ANFIS) for apparel manufacturers. The purpose of this paper is constructing an effective demand forecasting system for apparel manufacturers.
Design/methodology/approach
The ANFIS is used forecasting the demand for apparel manufacturers.
Findings
The results of the proposed study showed that an ANFIS-based demand forecasting system can help apparel manufacturers to forecast demand accurately, effectively and simply.
Originality/value
ANFIS is a new technique for demand forecasting, combines the learning capability of the neural networks and the generalization capability of the fuzzy logic. In this study, the demand is forecasted in terms of apparel manufacturers by using ANFIS. The input and output criteria are determined based on apparel manufacturers' requirements and via literature research and the forecasting horizon is about one month. The study includes the real-life application of the proposed system, and the proposed system is tested by using real demand values for apparel manufacturers.