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16 Nov 2012
Design of a neurofuzzy–regression expert system to estimate cost in a flexible jobshop automated manufacturing system
 Hamed Fazlollahtabar,
 Nezam MahdaviAmiri
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We propose a cost estimation model based on a fuzzy rule backpropagation network, configuring the rules to estimate the cost under uncertainty. A multiple linear regression analysis is applied to analyze the rules and identify the effective rules for cost estimation. Then, using a dynamic programming approach, we determine the optimal path of the manufacturing network. Finally, an application of this model is illustrated through a numerical example showing the effectiveness of the proposed model for solving the cost estimation problem under uncertainty.
 Abdi H, Valentin D, Edelman B, O’Toole AJ (1996) A Widrow–Hoff learning rule for a generalization of the linear autoassociator. J Math Psychol 40:175–182 CrossRef
 Bellman R (1957) Dynamic programming. Princeton University Press, Princeton
 Bellman R (2003) Dynamic programming. Dover Publications, New York
 BenArieh D, Qian L (2003) Activitybased cost management for design and development stage. Int J Prod Econ 83:169–183 CrossRef
 Cavalieria S, Maccarroneb P, Pinto R (2004) Parametric vs. neural network models for the estimation of production costs: a case study in the automotive industry. Int J Prod Econ 91:165–177 CrossRef
 Cheng B, Titterington DM (1994) Neural networks: a review from a statistical perspective. Stat Sci 9(1):2–30 CrossRef
 Dai JB, Lee NKS (2012) Economic feasibility analysis of flexible material handling systems: a case study in the apparel industry. Int J Prod Econ 136(1):28–36 CrossRef
 Deng S, Yeh TH (2011) Using least squares support vector machines for the airframe structures manufacturing cost estimation. Int J Prod Econ 131(2):701–708 CrossRef
 Dreyfus SE, Law AM (1977) The art and theory of dynamic programming. Academic, New York
 Dvir D, BenDavidb A, Sadehb A, Shenhar AJ (2006) Critical managerial factors affecting defense projects success: a comparison between neural network and regression analysis. Eng Appl Artif Intel 19:535–543 CrossRef
 Feng CX, Wang V (2002) Surface roughness predictive modeling: neural networks versus regression. IIE Trans Des Manuf 40(3):683–697
 Feng CX, Wang X (2002) Digitizing uncertainty modeling for reverse engineering applications: regression versus neural networks. J Intell Manuf 13(3):189–199 CrossRef
 Finnie GR, Wittig GE, Desharnais JM (1997) A comparison of software effort estimation techniques: using function points with neural networks, casebased reasoning and regression models. J Syst Softw 39(3):281–289 CrossRef
 Gallinari P, Thiria S, Badran F, FogelmanSoulie F (1991) On the relations between discriminant analysis and multilayer perceptrons. Neural Netw 4(3):349–360 CrossRef
 Gayretli A, Abdalla HS (1999) A featured based prototype system for the evaluation and optimization of manufacturing processes. Comput Ind Eng 37:481–484 CrossRef
 Ghasemzadeh S, Behrangi E, Azgomi MA (2009) Conflictfree scheduling and routing of automated guided vehicles in mesh topologies. Robot Auton Syst 30:738–748 CrossRef
 Heiat A (2002) Comparison of artificial neural network and regression models for estimating software development effort. Inf Softw Technol 44(15):911–922 CrossRef
 Ibaraki T (1987) Enumerative approaches to combinatorial optimization—part II. Ann Oper Res 11:397–439 CrossRef
 Khayat GE, Lagevin A, Riopel D (2006) Integrated production and material handling scheduling using mathematical programming and constraint programming. Eur J Oper Res 175:1818–1832 CrossRef
 Kim GH, An SH, Kang KI (2004) Comparison of construction cost estimating models based on regression analysis, neural networks, and casebased reasoning. Build Environ 39(10):1235–1242 CrossRef
 Klir GJ, Yuan B (1995) Fuzzy sets and fuzzy logic: theory and applications. PrenticeHall, US
 Krishnaswamy M, Krishnan P (2002) Nozzle wear rate prediction using regression and neural network. Biosyst Eng 82(1):53–64 CrossRef
 Lee J, Um K (2000) A comparison in a backbead prediction of gas metal arc welding using multiple regression analysis and artificial neural network. Opt Lasers Eng 34(3):149–158 CrossRef
 Marsten RE, Morin TL (1978) A hybrid approach to discrete mathematical programming. Math Program 14:21–40 CrossRef
 Martello S, Pisinger D, Toth P (1999) New trends in exact algorithms for the 0–1 knapsack problem. Eur J Oper Res 123:325–336 CrossRef
 McKim RA (1993) Neural network applications to cost engineering. Cost Eng 35:31–35
 Niazi A, Dai JS, Balabani S, Seneviratne L (2006) Product cost estimation: technique classification and methodology review. J Manuf Sci Eng 128:563–575 CrossRef
 Nolan A (1998) An intelligent system for case review and risk assessment in social services. AI Mag 19(1):39–46
 Pendharkar PC (2006) Scale economies and production function estimation for objectoriented software component and source code documentation size. Eur J Oper Res 172:1040–1050 CrossRef
 Plateau G, Elkihel M (1985) A hybrid method for the 0–1 knapsack problem. Meth Oper Res 49:277–293
 Quintana G, Ciurana J (2011) Cost estimation support tool for vertical high speed machines based on product characteristics and productivity requirements. Int J Prod Econ 134(1):188–195 CrossRef
 Rehman S, Guenov MD (1998) A methodology for modeling manufacturing costs at conceptual design. Comput Ind Eng 35:623–626 CrossRef
 Ripley BD (1994) Neural networks and related methods for classification. J Roy Stat Soc B (Methodological) 56(3):409–456
 Roy R, Souchoroukov P, Shehab E (2011) Detailed cost estimating in the automotive industry: data and information requirements. Int J Prod Econ 133(2):694–707 CrossRef
 Sarle WS (1994) Neural networks and statistical models. In Proceedings of the 19th annual SAS users group international conference, pp. 215–221
 Schumacher M, Robner R, Vach W (1996) Neural networks and logistic regression: part I. Comput Stat Data Anal 21(6):661–682 CrossRef
 Setyawati BR, Sahirman S, Creese RC (2002) Neural networks for cost estimation. AACE Int Trans EST13:13.1–13.8
 Shehab EM, Abdalla HS (2001) Manufacturing cost modeling for concurrent product development. Robot Comput Integrated Manuf 17:341–353 CrossRef
 Shuhui L, Wunsch DC, O’Hair E, Giesselmann MG (2001) Comparative analysis of regression and artificial neural network models for wind turbine power curve estimation. J Sol Energ Eng 123:327–332 CrossRef
 Smith AE, Mason AK (1997) Cost estimation predictive modeling: regression versus neural network. Eng Econ 42(2):137–161 CrossRef
 Toth P (1980) Dynamic programming algorithms for the zero–one knapsack problem. Computing 25:29–45 CrossRef
 Turban E, Aronson J (1998) Decision support systems and intelligent systems. PrenticeHall, London
 Vach W, Robner R, Schumacher M (1996) Neural networks and logistic regression: part II. Comput Stat Data Anal 21(6):683–701 CrossRef
 Vis IFA (2006) Survey of research in the design and control of automated guided vehicle systems. Eur J Oper Res 170:677–709 CrossRef
 Vrbsky SV (1997) A data model for approximate query processing of real time database. Data Knowl Eng 21:79–102 CrossRef
 Warner B, Misra M (1996) Understanding neural networks as statistical tools. Am Stat 50(4):284–293
 Weiss N, Kulikowski I (1991) Computer systems that learn. Morgan Kaufman, California
 Wu N, Zhou M (2005) Modeling and deadlock avoidance of automated manufacturing systems with multiple automated guided vehicles. IEEE Trans Syst Man Cybern B 35:1193–1201 CrossRef
 Yang CO, Lin TS (1997) Developing an integrated framework for featurebased early manufacturing cost estimation. Int J Adv Manuf Technol 13:618–629 CrossRef
 Yesilnacar E, Topal T (2005) Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Eng Geol 79(3–4):251–266 CrossRef
 Zhengrong G, Lam LH, Dhurjati PS (1996) Feature correlation method for enhancing fermentation development: a comparison of quadratic regression with artificial neural networks. Comput Chem Eng 20(Supp 1):S407–S412
 Zimmermann HJ (1996) Fuzzy set theory and its applications, 3rd edn. Kluwer Academic, Boston CrossRef
 Title
 Design of a neurofuzzy–regression expert system to estimate cost in a flexible jobshop automated manufacturing system
 Journal

The International Journal of Advanced Manufacturing Technology
Volume 67, Issue 58 , pp 18091823
 Cover Date
 20130701
 DOI
 10.1007/s0017001246105
 Print ISSN
 02683768
 Online ISSN
 14333015
 Publisher
 Springer London
 Additional Links
 Topics
 Keywords

 Automated manufacturing system
 Cost estimation
 Neural network
 Fuzzy logic
 Regression analysis
 Industry Sectors
 Authors

 Hamed Fazlollahtabar ^{(1)}
 Nezam MahdaviAmiri ^{(2)}
 Author Affiliations

 1. Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran
 2. Faculty of Mathematical Sciences, Sharif University of Technology, Tehran, Iran