Hybrid Uncertainties Modeling for Production Planning Problems

Hamijah Mohd. Rahman, Nureize Arbaiy, Pei-Chun Lin

Abstract


The formulated mathematical model needs pre-determined and precise model parametersto find a solution. However, the model parameters such as coefficient value are usually not precisely known. Coefficient plays a pivotal role sincethe coefficientcouldprovide important information in relationship between algebraic and linguistic expression. Existing method which is commonly used to generate the precise parametric valuesis unable to handle the coexistence of fuzzy information. Moreover, selecting real numbers for coefficients in random process increases the complexity inprogramming process. Hence, we proposed a fuzzy random regression method in this paper to estimate the precise coefficient values which contains fuzzy random information. An illustrative numerical example is provided to deduce coefficient values from different data representation which included the fuzziness and randomness.The coefficients were treated based on the property of fuzzy random regression. The approach results show that we have the significant capabilities to estimate the coefficient value and improve the model which retain the simultaneous uncertainties and set up in production planning problem.

Keywords


Production planning; Coefficient estimation; Hybrid uncertainties; Fuzzy random variable; Fuzzy random regression

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References


S. Abdollahzadeh, M. Rezaei, M. Dabbaghian and A. Norouzi, A goal programming model for computation of fuzzy linear regression with least error, in: Computer Engineering and Technology (ICCET), 2010 2nd International Conference on, 493 (2010).

F. Denisa, Bottleneck management in discrete batch production, Journal of Competitiveness 4 (2) (2012), 161 – 171.

G. González-Rodríguez, Á. Blanco, A. Colubi and M.A. Lubiano, Estimation of a simple linear regression model for fuzzy random variables, Fuzzy Sets and Systems 160 (3) (2009), 357 – 370.

S.C. Graves, Manufacturing planning and control, in: Handbook of Applied Optimization, Oxford University Press, 728 – 746 (1999).

H. Kwakernaak, Fuzzy random variables – I, Definitions and theorems, Information Sciences 15 (1) (1978), 1 – 29.

P.-C. Lin, J. Watada and B. Wu, A parametric assessment approach to solving facility location problems with fuzzy demands, IEEJ Transactions on Electronics, Information and Systems 9 (5)

(2014), 484 – 493.

P.-C. Lin, J. Watada and B. Wu, Risk assessment of a portfolio selection model based on a fuzzy statistical test, IEICE Transactions on Information and Systems E96-D (3) (2013), 579 – 588.

Malaysian Investment Development Authority, Rubber-based industry, retrieved on October 10, 2013 from http://www.mida.gov.my

Malaysian Rubber Board, Natural Rubber Statistic, retrieved September 1, 2013 from http: //www.lgm.gov.my

Malaysian Rubber Export Promotion Council, Rubber Statistical Bulletin, retrieved on October 10, 2013 from http://www.mrepc.gov.my

Market Watch 2012, The Rubber Sector in Malaysia, retrieved October 22, 2013 from http://www.malaysia.ahk.de

A. Messac, W.M. Batayneh and A. Ismail-Yahaya, Production planning optimization with physical programming, Engineering Optimization 34 (4) (2002), 323 – 340.

D.C. Montgomery, E.A. Peck and G.G. Vining, Introduction to Linear Regression Analysis, 821, Wiley (2012).

W. Näther, Regression with fuzzy random data, Computational Statistics & Data Analysis 51 (1) (2006), 235 – 252.

A. Nureize and J. Watada and S. Wang, Fuzzy random regression based multi-attribute evaluation and its application to oil palm fruit grading, Annals of Operations Research 219 (1) (2014), 299 – 315.

A. Nureize and J. Watada, A fuzzy regression approach to a hierarchical evaluation model for oil palm fruit grading, Fuzzy Optimization and Decision Making 9 (1) (2010), 105 – 122.

A. Nureize and J. Watada, Multi-level multi-objective decision problem through fuzzy random regression based objective function, in: Fuzzy Systems (FUZZ), 2011 IEEE International Conference

on IEEE (2011).

A.G. Sarip, B.H. Muhammad and M.N. Daud, Application of fuzzy regression model for real estate price prediction, Malaysian Journal of Computer Science 29 (1) (2016), 15 – 27.

A.F. Shapiro, Fuzzy Regression Models, Article of Penn State University (2005).

H. Tanaka, H. Isao and J. Watada, Possibilistic linear regression analysis for fuzzy data, European Journal of Operational Research 40 (3) (1989), 389 – 396.

S.A. Torabi, M. Ebadian and R. Tanha, Fuzzy hierarchical production planning (with a case study), Fuzzy Sets and Systems 161 (11) (2010), 1511 – 1529.

J. Watada, Building models based on environment with hybrid uncertainty, in: Modeling, Simulation and Applied Optimization (ICMSAO), 2011, 4th International Conference on. IEEE (2011).

J. Watada, S. Wang and W. Pedrycz, Building confidence-interval-based fuzzy random regression models, Fuzzy Systems, IEEE Transactions on 17 (6) (2009), 1273 – 1283.

M.S. Yang and C.H. Ko, On cluster-wise fuzzy regression analysis, Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on 27 (1) (1997), 1 – 13.


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