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Purpose -The purpose of this paper is to effectively deal with querying of classification with membership. Design/methodology/approach -The authors propose a scheme comprising a layer of Bloom filter for membership checking and a second layer based on neural network for dealing with the classification requirement. Findings -Not only could false positives be dramatically decreased, but also classification could be achieved with the proposed scheme.Research limitations/implications -The experimental data were randomly generated instead of real-world ones. Practical implications -It is difficult to implement this scheme in a real-world environment, such as the internet. Second, the neural network requires time to converge to a satisfactory level. Social implications -Internet ethic might be compromised by hackers once they find a way around the filtering mechanism. Originality/value -The neural network was moditified and utilized for the first time to be suitable for our purpose. Second, the two-layer design shows effectiveness.

Rapid growth of raw material usage has resulted in supply shortages and soaring prices worldwide, where eco-solutions become promisingly important and thus a trend. Under such a situation and a heavily competitive environment, enterprises desiring to remain current are required not only to enhance their competitiveness but, more importantly, to reduce their costs. Reverse logistics is a ecostrategy for reducing production cost; however, it accompanies with an even higher managerial counterpart. In this research, we establish a model in which prediction and decision supporting are conducted in order to greatly reduce managerial cost as well as increase performances. The model integrates a vendor manage inventory concept, the UHF RFID technology, a simulation system (e.g. eMPlant) and a neural-based predicting component for achieving the goal of reducing managerial cost. With the proposed model, enterprises could determine the indices of recycling, production and scheduling, and therefore a dynamic approach for estimating shortages at each workstation can be accomplished, which could dramatically decrease the cost suffered from routing, inventory and resource allocations. Future work will be focused on optimization of the estimated indices, where a decision supporting system for reverse logistics could be accomplished.