A hybrid system by evolving case-based reasoning with genetic algorithm in wholesaler's returning book forecasting

Authors:
Pei-Chann Chang;Chien-Yuan Lai;K. Robert Lai
Affiliations:
Department of Industrial Engineering and Management, Yuan Ze University, Taoyuan, Taiwan, R.O.C.;Department of Industrial Engineering and Management, Yuan Ze University, Taoyuan, Taiwan, R.O.C.;Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, Taiwan, R.O.C.
Venue:
Decision Support Systems
Year:
2006

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Abstract

A hybrid system by evolving a Case-Based Reasoning (CBR) system with a Genetic Algorithm (GA) is developed for wholesaler's returning book forecasting. For a new book, key factors, such as the grade of the author, the grade of publisher, hot or slow season of publication date, sale volumes for the first 3 months and the returning rate, have been identified and applied as the key features to calculate the similarity coefficient of a new release book and to retrieve similar book from the reference cases to justify if the new book is a slow-selling or selling book. The case base of this research is acquired from a book wholesaler in Taiwan, and it is applied by the hybrid system to forecast returning books. The results of the prediction of the hybrid system were compared with the results of a back propagation neural network (BPNN), a conventional CBR, and a multiple-regression analysis method. The experimental results show that the GA/CBR is more accurate and efficient when being applied to the forecast of the returning books than other methods.