A decision support system for order selection in electronic commerce based on fuzzy neural network supported by real-coded genetic algorithm

Abstract

This research attempts to develop a decision support system for order selection. The proposed system is able to integrate both the quantitative and qualitative factors together. For the qualitative factors, the fuzzy IF–THEN rules are summarized from the questionnaire survey for the production experts and learned by a proposed fuzzy neural network (FNN) with initial weights generated by real-coded genetic algorithm (GA). Then, a feedforward artificial neural network (ANN) with error back-propagation (EBP) learning algorithm is employed to integrate the above two parts together. Both the simulation and real-life problem provided by an internationally OEM company results show that the proposed FNN can well learn the fuzzy IF–THEN rules. In addition, real-coded GA is proved to be better than the binary GA both in speed and accuracy. Considering both the quantitative and qualitative factors has more accurate results compared with considering only the quantitative factors.