nach oben

Journal of Intelligent Manufacturing

Erschienen in:

04.09.2015

Comparison of fuzzy inference system (FIS), FIS with artificial neural networks (FIS + ANN) and FIS with adaptive neuro-fuzzy inference system (FIS + ANFIS) for inventory control

verfasst von: Prasert Aengchuan, Busaba Phruksaphanrat

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 4/2018

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Conventional inventory models mostly cope with a known demand and adequate supply, but are not realistic for many industries. In this research, the fuzzy inference system (FIS) model, FIS with artificial neural network (ANN) model and FIS with adaptive neuro-fuzzy inference system (ANFIS) model in which both supply and demand are uncertain were applied for the inventory system. For FIS model, the generated fuzzy rules were applied to draw out the fuzzy order quantity continuously. The order quantity was adjusted according to the FIS model with the evaluation algorithm for the inventory model. The output of FIS model was also used as data for FIS + ANN and FIS + ANFIS models. The FIS + ANFIS model was studied with three membership functions; trapezoidal and triangular (Trap), Gaussian and bell shape. Inventory costs of the proposed models were compared with the stochastic economic order quantity (EOQ) models based on previous data of a case study factory. The results showed that the FIS + ANFIS_Gauss model gave the best performance of total inventory cost saving by more than 75 % compared to stochastic EOQ model.

Vorheriger Artikel An improved ant colony algorithm for dynamic hybrid flow shop scheduling with uncertain processing time

Nächster Artikel A modal interval based method for dynamic decision model considering uncertain quality of used products in remanufacturing

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Aengchuan, P., & Phruksaphanrat, B. (2013). Inventory system design by fuzzy logic control: A case study. Advanced Materials Research, 8, 619–624.CrossRef

Andriolo, A., Battini, D., Grubbstrom, R. W., & Persona, A. (2014). A century of evolution from Harris’s basic lot size model: Survey and research agenda. The International Journal of Production Economics, 155, 16–38.CrossRef

Aloulou, M. A., Dolgui, A., & Kovalyov, M. Y. (2014). A bibliography of non-deterministic lot-sizing models. International Journal of Production Research, 52(8), 2293–2310.CrossRef

Azedegan, A., Porobic, L., Ghazinoory, S., Samouei, P., & Kheirkhah, A. S. (2011). Fuzzy logic in manufacturing: A review of literature and a specialized application. International Journal of Production Economics, 132(2), 258–270.CrossRef

Azizi, A., Ali, A. Y., & Ping, L. W. (2013). An adaptive neuro-fuzzy inference system for a dynamic production environment under uncertainties. World Applied Sciences Journal, 25(3), 428–433.

Bai, Y., & Wang, D. (2006). Fundamentals of Fuzzy Logic Control–Fuzzy Sets, Fuzzy Rules and Defuzzifications. In Advanced Fuzzy Logic Technologies in Industrial Applications (pp. 17–36). Springer London.

Camastra, F., Ciaramella, A., Giovannelli, V., Lener, M., Rastelli, V., Staiano, A., et al. (2015). A fuzzy decision system for genetically modified plant environmental risk assessment using Mamdani inference. Expert Systems with Applications, 42, 1710–1716.CrossRef

Castillo, O., & Melin, P. (2008). Type-2 fuzzy logic theory and applications. Berlin: Springer.

Chaudhary, H., Panwar, V., Prasad, R., & Sukavanam, N. (2014). Adaptive neuro fuzzy based hybrid force/position control for an industrial robot manipulator. Journal Of Intelligent Manufacturing. doi:10.1007/s10845-014-0952-1.

Chede, B., Jain, C. K., Jain, S. K., & Chede, A. (2012). Fuzzy logic analysis based on inventory considering demand and stock quantity on hand. Industrial Engineering Letters, 2(1), 13–21.

Chen, S. P. (2011). A membership function approach to lot size re-order point inventory problems in fuzzy environments. International Journal of Production Research, 49(13), 3855–3871.CrossRef

Chen, S. H., Wang, C. C., & Ramer, A. (1996). Backorder fuzzy inventory model under function principle. Information Sciences, 95, 71–79.CrossRef

Chung, C. J., Widyadana, G. A., & Wee, H. M. (2011). Economic production quantity model for deteriorating inventory with random machine unavailability and shortage. International Journal of Production Research, 49(3), 883–902.CrossRef

Esen, H., Inalli, M., Sengur, A., & Esen, M. (2008a). Forecasting of a ground-coupled heat pump performance using neural networks with statistical data weighting pre-processing. International Journal of Thermal Sciences, 47(4), 431–441.CrossRef

Esen, H., Inalli, M., Sengur, A., & Esen, M. (2008b). Performance prediction of a ground-coupled heat pump system using artificial neural networks. Expert Systems with Applications, 35, 1940–1948.CrossRef

Esen, H., Inalli, M., Sengur, A., & Esen, M. (2008c). Modelling a ground-coupled heat pump system using adaptive neuro-fuzzy inference systems. International Journal of Refrigeration, 31(1), 65–74.CrossRef

Esen, H., Inalli, M., Sengur, A., & Esen, M. (2008d). Artificial neural networks and adaptive neuro-fuzzy assessments for ground-coupled heat pump system. Energy and Buildings, 40(6), 1074–1083.CrossRef

Esen, H., Inalli, M., Sengur, A., & Esen, M. (2008e). Predicting performance of a ground-source heat pump system using fuzzy weighted pre-processing-based ANFIS. Building and Environment, 43(12), 2178–2187.CrossRef

Esen, H., Ozgen, F., Esen, M., & Sengur, A. (2009). Artificial neural network and wavelet neural network approaches for modelling of a solar air heater. Expert Systems with Applications, 36(8), 11240–11248.CrossRef

Feng, G. (2010). Integrating dynamic pricing and replenishment decisions under supply capacity uncertainty. Management Science, 56(2), 2154–2172.CrossRef

Fragiadakis, N. G., Tsoukalas, V. D., & Papazoglou, V. J. (2014). An adaptive neuro-fuzzy inference system (anfis) model for assessing occupational risk in the shipbuilding industry. Safety Science, 63, 226–235.CrossRef

Glock, C. H., Grosse, E. H., & Ries, J. M. (2014). The lot sizing problem: A tertiary study. The International Journal of Production Economics, 155, 39–51.CrossRef

Gokulachandran, J., & Mohandas, K. (2015). Comparative study of two soft computing techniques for the prediction of remaining useful life of cutting tools. Journal of Intelligent Manufacturing, 26, 255–268. doi:10.1007/s10845-013-0778-2.CrossRef

Good, P. I. (1999). Resampling methods: A practical guide to data analysis. Boston: Birkhauser.CrossRef

Grubbstrom, R. W. (2014). Dynamic lot sizing with a finite production rate. International Journal of Production Economics’s, 149, 68–79.CrossRef

Guan, Y. (2011). Stochastic lot-sizing with backlogging: Computational complexity analysis. Journal of Global Optimization’s, 49, 651–678.CrossRef

Guillaume, R., Kobylanski, P., & Zielinski, P. (2012). A robust lot sizing problem with ill-known demands. Fuzzy Sets and Systems, 206, 39–57.CrossRef

Guner, H. A. A., & Yumuk, H. A. (2014). Application of a fuzzy inference system for the prediction of long shore sediment transport. Applied Ocean Research, 48, 162–175.CrossRef

Guneri, A. F., Ertay, T., & Yucel, A. (2011). An approach based on ANFIS input selection and modeling for supplier selection problem. Expert Systems with Applications, 38, 14907–14917.CrossRef

Handfield, R., Warsing, D., & Wu, X. (2009). (Q, r) Inventory policies in a fuzzy uncertain supply chain environment. European Journal of Operational Research, 197, 609–619.CrossRef

Harris, F. H. (1913). How many parts to make at once. Factory, The Magazine of Management, 10(2), 135–136, 152.

Hosoz, M., Ertunc, H. M., & Bulgurcu, H. (2011). An adaptive neuro-fuzzy inference system model for predicting the performance of a refrigeration system with a cooling tower. Expert Systems with Applications, 38, 14148–14155.

Jang, J. S. R. (1993). ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, Cybernetics, 23, 665–685.CrossRef

Jha, M. N., Pratihar, D. K., Bapat, A. V., Dey, V., Ali, M., & Bagchi, A. C. (2014). Knowledge-based systems using neural networks for electron beam welding process of reactive material (Zircaloy-4). Journal of Intelligent Manufacturing, 25, 1315–1333. doi:10.1007/s10845-013-0732-3.CrossRef

Kamal, L., & Sculfort, J.-L. (2007). Fuzzy modelling of inventory control system in uncertain environment. International Symposium on Logistics and Industrial Informatics, 53–57. doi:10.1109/LINDI.2007.4343512.

Kang, H.-Y., & Lee, A. H. I. (2013). A stochastic lot-sizing model with multi-supplier and quantity discounts. International Journal of Production Research, 51(1), 245–263.CrossRef

Khan, M., Jaber, M. Y., & Wahab, M. I. M. (2010). Economic order quantity model for items with imperfect quality with learning in inspection. International Journal of Production Economics, 124(1), 87–96.CrossRef

Kiran, T. R., & Rajput, S. P. S. (2011). An effectiveness model for an indirect evaporative cooling (IEC) system: Comparison of artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS) and fuzzy inference system (FIS) approach. Applied Soft Computing, 11, 3525–3533.CrossRef

Kocyigit, N. (2015). Fault and sensor error diagnostic strategies for a vapor compression refrigeration system by using fuzzy inference systems and artificial neural network. The International Journal of Refrigeration, 50, 69–79.CrossRef

Kovac, P., Rodic, D., Pucovsky, V., Savkovic, B., & Gostimirovic, M. (2013). Application of fuzzy logic and regression analysis for modeling surface roughness in face milliing. Journal of Intelligent Manufacturing, 24, 755–762. doi:10.1007/s10845-012-0623-z.CrossRef

Kuo, C.-F. J., Hsu, C.-T. M., Liu, Z.-X., & Wu, H.-C. (2014). Automatic inspection system of LED chip using two-stages back-propagation neural network. Journal of Intelligent Manufacturing, 25, 1235–1243. doi:10.1007/s10845-012-0725-7.CrossRef

Kuo, R. J., Tseng, Y. S., & Chen, Z.-Y. (2014). Integration of fuzzy neural network and artificial immune system-based back-propagation neural network for sales forecasting using qualitative and quantitative data. Journal Of Intelligent Manufacturing. doi:10.1007/s10845-014-0944-1.

Lee, H., & Yao, J. (1999). Economic order quantity in fuzzy sense for inventory without backorder model. Fuzzy Sets and Systems, 105(1), 13–31.CrossRef

Lee, S.-D., Yang, C.-M., & Lan, S.-C. (2014). Economic lot sizing in a production system with random demand. International Journal of Systems Science. doi:10.1080/00207721.2014.915354.

Lenart, B., Grzybowska, K., & Cimer, M. (2012). Adaptive inventory control in production systems. In E. Corchado et al. (Eds.), HAIS 2012, Part II, LNCS. 7209. Springer: Berlin, Heidelberg (pp. 222–228).

Li, H., & Thorstenson, A. (2014). A multi-phase algorithm for a joint lot-sizing and pricing problem with stochastic demands. International Journal of Production Research, 52(8), 2345–2362.CrossRef

Mahata, G. C., & Goswami, A. (2013). Fuzzy inventory models for items with imperfect quality and shortage backordering under crisp and fuzzy decision variables. Computer & Industrial Engineering, 64, 190–199.CrossRef

Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with fuzzy logic controller. International Journal of Man-Machine Studies, 7, 1–13.CrossRef

Melin, P., Soto, J., Castillo, O., & Soria, J. (2012). A new approach for time series prediction using ensembles of ANFIS models. Expert Systems with Applications, 39, 3494–3506.CrossRef

Meredith, J., & Shafer, S. (2011). Operations management (4th ed.). Hoboken: Wiley.

Mondal, S., & Maiti, M. (2002). Multi-item fuzzy EOQ models using genetic algorithm. Computers & Industrial Engineering, 44(1), 105–117.CrossRef

Nasrollahzadeh, K., & Basiri, M. M. (2014). Prediction of shear strength of FRP reinforced concrete beams using fuzzy inference system. Expert Systems with Applications, 41, 1006–1020.CrossRef

Park, K. S. (1987). Fuzzy set theoretic interpretation of economic order quantity. IEEE Transactions on Systems, Man and Cybernetics, 6(17), 1082–1084.newpage

Parlar, M., & Perry, D. (1995). Analysis of a (Q, r, T) inventory policy with deterministic and random yields when future supply is uncertain. European Journal of Operational Research, 84, 431–443.CrossRef

Phootrakornchai, W., & Jiriwibhakorn, S. (2015). Online critical clearing time estimation using an adaptive neuro-fuzzy inference system (ANFIS). Electrical Power and Energy Systems, 73, 170–181.CrossRef

Porteus, E. L. (1986). Optimal lot sizing, process quality improvement and setup cost reduction. Operations Research, 34(1), 137–144.CrossRef

Razani, M., Yazdani-Chamzini, A., & Yakhchali, S. H. (2013). A novel fuzzy inference system for predicting roof fall rate in underground coal mines. Safety Science, 55, 26–33.CrossRef

Rong, L. (2010). Uncertain economic order quantity model with uncertain costs. Journal of Information and Computational Science, 7(13), 2723–2730.

Rothstein, A. P., & Rakityanskaya, A. B. (2006). Inventory control as an identification problem based on fuzzy logic. Cybernetics and Systems Analysis, 42(3), 411–419.CrossRef

Samanta, B., & Al-Araimi, S. A. (2001). An inventory control model using fuzzy logic. The International Journal of Production Economics, 73, 217–226.CrossRef

Samanta, B., & Al-Araimi, S. A. (2003). Application of an adaptive neuro-fuzzy inference system in inventory control. International Journal of Smart Engineering System Design, 5(4), 547–553.CrossRef

Sana, S. S. (2011). Price-sensitive demand for perishable items—An EOQ model. Applied Mathematics and Computation, 217(13), 6248–6259.CrossRef

Seneyigit, E., & Erol, R. (2010). New lot sizing heuristics for demand and price uncertainties with service-level constraint. International Journal of Production Research, 48(1), 21–44.CrossRef

Silver, E. A., & Meal, H. A. (1973). A heuristic for selecting lot sizing requirements for the case of a deterministic time varying demand rate and discrete opportunities for replenishment. Production and Inventory Management, 14(2), 64–74.

Sommer, G. (1981). Fuzzy inventory scheduling. In G. Lasker (Ed.), Applied systems and cybernetics. New York: Pergamon Press.

Sumathi, S., & Paneerselvam, S. (2010). Computational intelligence paradigms: Theory and applications using MATLAB. Boca Raton, FL: CRC Press, Inc.

Taft, E. W. (1918). The most economical production lot. The Iron Age, 101, 1410–1412.

Tanthatemee, T., & Phruksaphanrat, B. (2012). Fuzzy inventory control for uncertain demand and supply. Proceedings of the International MultiConference of Engineers and Scientists, 2, 1224–1229.

Tsai, K.-M., & Luo, H.-J. (2014). An inverse model for injection molding of optical lens using artificial neural network coupled with genetic algorithm. Journal of Intelligent Manufacturing. doi:10.1007/s10845-014-0999-z

Ullah, M., & Kang, C. W. (2014). Effect of rework, rejects and inspection on lot size with work-in-process inventory. International Journal Production Research, 52(8), 2448–2460.CrossRef

Wagner, H. M., & Whitin, T. M. (1958). Dynamic version of the economic lot size model. Manangement Science, 5(1), 89–96.CrossRef

Wang, B., Man, T., & Jin, H. (2015). Prediction of expansion behavior of self-stressing concrete by artificial neural networks and fuzzy inference systems. Construction and Building Materials, 84, 184–191.CrossRef

Wang, C.-H. (2010). Some remarks on an optimal order quantity and reorder point when supply and demand are uncertain. Computers & Industrial Engineering, 58, 809–813.CrossRef

Wee, H. M., & Widyadana, G. A. (2012). Economic production quantity models for deteriorating items with rework and stochastic preventive maintenance time. International Journal Production Research, 50(11), 2940–2952.CrossRef

Yang, L., & Entchev, E. (2014). Performance prediction of a hybrid microgeneration system using adaptive neuro-fuzzy inference system (ANFIS) technique. Applied Energy, 134, 197–203.CrossRef

Yang, W.-A., & Zhou, W. (2013). Autoregressive coefficient-invariant control chart pattern recognition in autocorrelated manufacturing processes using neural network ensemble. Journal of Intelligent Manufacturing. doi:10.1007/s10845-013-0847-6

Yimer, A.D., & Demirli, K. (2004). Fuzzy modeling and simulation of single item inventory system with variable demand. IEEE annual meeting of the North American fuzzy information processing society Banff, 2, Alberta, Canada (pp. 985–989).

Ying, L. C., & Pan, M. C. (2008). Using adaptive network based fuzzy inference system to forecast regional electricity loads. Energy Conversation and Management, 49, 205–211.CrossRef

Titel: Comparison of fuzzy inference system (FIS), FIS with artificial neural networks (FIS + ANN) and FIS with adaptive neuro-fuzzy inference system (FIS + ANFIS) for inventory control
verfasst von: Prasert Aengchuan
Busaba Phruksaphanrat
Publikationsdatum: 04.09.2015
Verlag: Springer US
Erschienen in: Journal of Intelligent Manufacturing / Ausgabe 4/2018
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI: https://doi.org/10.1007/s10845-015-1146-1

Premium Partner

Marktübersichten

Die im Laufe eines Jahres in der „adhäsion“ veröffentlichten Marktübersichten helfen Anwendern verschiedenster Branchen, sich einen gezielten Überblick über Lieferantenangebote zu verschaffen.

Zur Marktübersicht

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 4/2018

A modal interval based method for dynamic decision model considering uncertain quality of used products in remanufacturing

A new arc detection method based on fuzzy logic using S-transform for pantograph–catenary systems

Fault feature extraction of rolling element bearings based on wavelet packet transform and sparse representation theory

An effective L-MONG algorithm for solving multi-objective flow-shop inverse scheduling problems

Residual life prediction based on dynamic weighted Markov model and particle filtering

An improved ant colony algorithm for dynamic hybrid flow shop scheduling with uncertain processing time

Premium Partner

Marktübersichten