Fuzzy-Neural Network Models for Effective Control of Profitability
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As the demand for materials continues to grow and the supply of natural resources continues to dwindle, a high degree of recycling of materials has become increasingly important. Recycling industries need to overcome the high cost of recycling and improve profitability by effective management of resources and management practices, using decision support models that are both quantitative and qualitative. However, models in present use emphasize mathematical procedures which are only good for analyzing quantitative decision variables and fail to take into consideration several relevant qualitati...
As the demand for materials continues to grow and the supply of natural resources continues to dwindle, a high degree of recycling of materials has become increasingly important. Recycling industries need to overcome the high cost of recycling and improve profitability by effective management of resources and management practices, using decision support models that are both quantitative and qualitative. However, models in present use emphasize mathematical procedures which are only good for analyzing quantitative decision variables and fail to take into consideration several relevant qualitative decision variables which can not be simply quantified. This book provides means of handling qualitative decision variables problems in profitability control using fuzzy logic and neural network tools to solve the lapses in other models. To reinforce the work, it is applied to a case study performed on a Paper recycling industry in Nigeria. Comparative analyses indicate that fuzzy-neural network offers a better correlation than the fuzzy logic components and should be useful in handling qualitative decision variable problems and should provide a better solution to the profitability control.
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