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.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
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.
Umoh U. A. had received a Ph.D. degree in Soft Computing, MSc. degree in DBMS and BSc. degree. She is currently working as a Senior Lecturer, in the University of Uyo, Dept. of Computer Sc. She has published several quality articles and written some quality books in computer discipline. She is a member of CPN, NCS, NIWIT, OWSD, UACEE, SCRG, etc.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 328 pp. Englisch. N° de réf. du vendeur 9786202008662
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Vendeur : moluna, Greven, Allemagne
Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Umoh UduakUmoh U. A. had received a Ph.D. degree in Soft Computing, MSc. degree in DBMS and BSc. degree. She is currently working as a Senior Lecturer, in the University of Uyo, Dept. of Computer Sc. She has published several quality. N° de réf. du vendeur 159930552
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Vendeur : Revaluation Books, Exeter, Royaume-Uni
Paperback. Etat : Brand New. 328 pages. 8.66x5.91x0.74 inches. In Stock. N° de réf. du vendeur zk6202008660
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Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 328 pp. Englisch. N° de réf. du vendeur 9786202008662
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Vendeur : preigu, Osnabrück, Allemagne
Taschenbuch. Etat : Neu. Fuzzy-Neural Network Models for Effective Control of Profitability | Uduak Umoh | Taschenbuch | 328 S. | Englisch | 2017 | LAP LAMBERT Academic Publishing | EAN 9786202008662 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. N° de réf. du vendeur 109605329
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Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. N° de réf. du vendeur 9786202008662
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