An in-depth comparison with the previous survey findings and potential future research trend in the neural network business research are discussed.ĭue to the breakthrough of the neural network technology, there has been an increasing amount of neural network application research in the last decade. Information on neural network development language/tool, learning paradigm, computing operating environment, journals and authors are included. Production/operations, finance, marketing/distribution, and information systems were found as the most popular application areas. A classification of these articles by year reveals that a large amount of research has been published in the last five years.
Our extensive literature searches have identified a total of 302 research articles. The purpose of this paper is to present a comprehensive bibliography of neural network application research in business during the period of 1994–1998. Utilizing Genetic Algorithm (GA) will help to further refine or adapt the weights of the NN which optimizes target selection strategy for a specific problem. Even at the end of the training the NN, we may end up with local optima or the NN which is too general to specific problems. Back propagation algorithm is used first, then Adaptive Resonance Theory (ART) network is employed to reduce training time since new rules come up often. Therefore, Neural Network is employed because of its capability of learning as well as dealing with fuzzy data. However, these membership function can not be justified and can be very subjective. Expert systems are brittle in its nature, and Fuzzy rulebase needs membership functions defined for each linguistic variable. Expert systems and Fuzzy rulebase as well as Neural Network (NN) have been considered as alternatives for human experts.
The current version of EH highly depends on experts' intuition in selecting a target. Selecting the most promising target constitutes the success of EH.
The Exchange Heuristic (EH) has demonstrated superior results compared with other RCS methods in solving Resource Constrained Scheduling (RCS) problems.
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