Waste flows management by their prediction in a production company

• Autorzy: Scherer M.

Identyfikatory

DOI

10.17512/jamcm.2017.2.11

• Języki publikacji: EN

Abstrakty

EN

In this paper we apply neuro-fuzzy systems to predict waste production in a company. Waste is produced by companies at every phase of their business, e.g. at the stage of supply, production and distribution. We used data on the production waste of one of the typical Polish manufacturing companies operating in the automotive industry. We predicted monthly waste production by data-driven learning of neuro-fuzzy systems. Neuro-fuzzy systems share with artificial neural-networks the ability to learn from data and the interpretability with fuzzy systems. In the experiments we achieved a high rate of prediction.

Słowa kluczowe

EN

reverse logistics; artificial intelligence

PL

logistyka zwrotna; sztuczna inteligencja; systemy neuronowo-rozmyte

• Wydawca: Wydawnictwo Politechniki Częstochowskiej
• Czasopismo: Journal of Applied Mathematics and Computational Mechanics
• Rocznik: 2017
• Tom: Vol. 16, nr 2
• Strony: 135--144
• Opis fizyczny: Bibliogr. 16 poz., rys., tab.

Twórcy

autor

Scherer M.

• Department of Engineering Management, Czestochowa University of Technology Czestochowa, Poland

Bibliografia

• [1] Mesjasz-Lech A., Efektywność ekonomiczna i sprawność ekologiczna logistyki zwrotnej, Wydawnictwo Politechniki Częstochowskiej, Częstochowa 2012.
• [2] Szołtysek J., Twarog S., Logistyka zwrotna, Teoria i praktyka, Polskie Wydawnictwo Ekonomiczne, Warszawa 2017.
• [3] Klaus P., Logistics as a science of networks and flows, Logistics Research 2010, 2, 55-56.
• [4] Pokharel S., Mutha A., Perspectives in reverse logistics: A review, Resources, Conservation and Recycling 2009, 53.
• [5] Chang F.J., Chang Y.-T., Adaptive neuro-fuzzy inference system for prediction of water level in reservoir, Advances in Water Resources 2006, 29, 1, January, 1-10.
• [6] Neagoe V.E., Latin L.F., Grunwald S., A neuro-fuzzy approach to classification of ECG signals for ischemic heart disease diagnosis, AMIA Annual Symposium Proceedings, 2003, 494-498.
• [7] Lin C.S., Chou S., Weng S.M., Hsieh J.C., A final price prediction model for english auctions: a neuro-fuzzy approach, Quality & Quantity 2013, 47, 2, February, 599-613.
• [8] Cevik H., Cunkaş M., Short-term load forecasting using fuzzy logic and ANFIS, Neural Comput. Appl. 2015, 26, 6, August, 1355-1367.
• [9] Rivero C.R., Pucheta J., Laboret S., Sauchelli V., Patińo D., Energy associated tuning method for short-term series forecasting by complete and incomplete datasets, Journal of Artificial Intelligence and Soft Computing Research 2017, 7, 1, 5-16.
• [10] Konovalova N., Kristovska I., Kudinska M., Credit risk management in commercial banks, Polish Journal of Management Studies 2016, 13, 2, 90-100.
• [11] Stefko R., Gavurova B., Korony S., Efficiency measurement in healthcare work management using malmquist indices, Polish Journal of Management Studies 2016, 13, 1, 168-180.
• [12] Scherer R., Multiple Fuzzy Classification Systems, Springer 2012.
• [13] Scherer R, Rutkowski L., Neuro-Fuzzy Relational Systems, International Conference on Fuzzy Systems and Knowledge Discovery, November 18-22, 2002, Singapore, 44-48.
• [14] Prasad M., Liu Y.-T., Li D.L., Lin C.-T., Shah R.R., Kaiwartya O.P., A new mechanism for data visualization with TSK-type preprocessed collaborative fuzzy rule based system, Journal of Artificial Intelligence and Soft Computing Research 2017, 7, 1, 33-46.
• [15] Jang J.-S. R., Sun C.-T., Mizutani E., Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, Prentice Hall, 1996.
• [16] Szajt M., Przestrzeń w badaniach ekonomicznych, Sekcja Wydawnictw Wydziału Zarządzania, Częstochowa 2014.

Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).