Solving a posynomial geometric programming problem with fully fuzzy approach

Authors

  • Samira Kamaei Shahid Chamran University of Ahvaz, Faculty of Mathematical Sciences and Computer, Department of Mathematics, Ahvaz, Iran
  • Sareh Kamaei Shahid Chamran University of Ahvaz, Faculty of Mathematical Sciences and Computer, Department of Mathematics, Ahvaz, Iran
  • Mansour Saraj Shahid Chamran University of Ahvaz, Faculty of Mathematical Sciences and Computer, Department of Mathematics, Ahvaz, Iran

DOI:

https://doi.org/10.2298/YJOR181115005K

Keywords:

fuzzy logic, posynomial, geometric programming, optimization

Abstract

In this paper we have investigated a class of geometric programming problems in which all the parameters are fuzzy numbers. In fact, due to impreciseness of the cost components and exponents in geometric programming with their inherently behavior as in economics and many other areas, we have used fuzzy parametric geometric programming. Transforming the primal problem of fuzzy geometric programming into its dual and using the Zadeh's extension principle, we convert the dual form into a pair of mathematical programs. By applying the α-cut on the objective function and r-cut on the constraints in dual form of geometric programming, we obtain an acceptable (α, r) optimal values. Then, we further calculate the lower and upper bounds of the fuzzy objective with emphasize on modification of a method presented in [14, 32]. Finally, we illustrate the methodology of the approach with a numerical example to clarify the idea by drawing the different steps of LR representation of Zαr.

References

Allahviranloo, T., Lot, F. H., Kiasary, M.K., Kiani, N.A., and Alizadeh, L., Solving fully fuzzy linear programming problem by the ranking function, Applied Mathematical Sciences, (2) (2008) 19-32.

Bazikar, F., and Saraj, M., Solving linear multi-objective geometric programming problems via reference point approach, Sains Malaysiana, (43) (8) (2014) 1271-1274.

Bellman, R.E., and Zadeh, L.A., Decision-making in a fuzzy environment, Management Science, (17) (1970) 141-164.

Boyd, S., Kim, S.J., Patil, D.D., and Horowitz, M.A., Digital circuit optimization via geometric programming, Operation Research, (53) (6) (2005) 899-932.

Boyd, S.P., Kim, S. J., Vandenberghe, L., and Hossib, A., A tutorial on geometric programming, Optimization and Engineering, (8) (1) (2007) 67-127.

Cao, B. Y., Fuzzy geometric programming, Applied optimization, Kluwer Academic Publishers, Springer, Berlin, 2002.

Chen, L.H., and Tsai, F.C., Fuzzy goal programming with different importance and priorities, European Journal of Operational Research, (133) (2001) 548-556.

Chu, C., and Wong, D.F., VLSI circuit performance optimization by geometric programming, Annals of Operations Research, (105) (2001) 37-60.

Dun, R.J., Peterson, E.L., and Zener, C., Geometric programming theory and applications, Wiley, New York, 1967.

Ebrahimnejad, A., and Nasseri, S.H., Using complementary slackness property to solve linear programming with fuzzy parameters, Fuzzy Information and Engineering, (1) (2009) 233-245.

Ezzati, R., Khorram, E., and Enayati, R., A new algorithm to solve fully fuzzy linear programming problems using the MOLP problem, Applied Mathematical Modelling, (39) (2015) 3183-3193.

Kumar, A., Kaur, J., and Singh, P., A new method for solving fully fuzzy linear programming problems, Applied Mathematical Modelling, (35) (2011) 817-823.

Larbani, M., Multiobjective problems with fuzzy parameters and games against nature, Fuzzy Sets and Systems, (161) (2010) 2642-2660.

Liu, S.T., Geometric programming with fuzzy parameters in engineering optimization, International Journal of Approximate Reasoning, (46) (2007) 484-498.

Mahapatra, G.S., and Mahapatral, B.S., Reliability and Cost Analysis of Series System Models using Fuzzy Parametric Geometric Programming, Fuzzy Information and Engineering, (2) (4) (2010) 399-411.

Mahapatra, G.S., Mahapatra, B.S., and Roy, P.K., Fuzzy Decision-making on Reliability of Series System: Fuzzy Geometric Programming Approach, Annals of Fuzzy Mathematics and Informatics, (1) (1) (2011) 107-118.

Mahapatra, G.S., and Mandal, T.K., Posynomial parametric geometric programming with interval valued coefficient, Journal Optimization Theory and Applications, (154) (2012) 120-132.

Mahapatra, G.S., Mandal, T.K., and Samanta, G.P., A Production Inventory Model with Fuzzy Coefficients Using Parametric Geometric Programming Approach, International Journal of Machine Learning and Cybernetics, (2) (2) (2011) 99-105.

Mahapatra, G.S., Mandal, T.K., and Samanta, G.P., EPQ model with fuzzy coefficient of objective and constraint via parametric geometric programming, International Journal of Operational Research, (17) (4) (2013) 436-448.

Mahapatra, G.S., T.K. Mandal, and G.P. Samanta, Fuzzy parametric geometric programming with application in fuzzy EPQ model under flexibility and reliability consideration, Journal of Information and Computing Science, (7) (3) (2012) 223-234.

Mahapatra, G.S., and Roy, T.K., Single and Multi-container Maintenance Model: A Fuzzy Geometric Programming Approach, Journal of Mathematics Research, (1) (2) (2009) 47-60.

Mendoca, L.F., Sousa, J.M., and Sada Costa, J.M.G., Optimization problems in multivariable fuzzy predictive control, International Journal of Approximate Reasoning, (36) (2004) 199-221.

Nehi, H.M., Maleki, H.R., and Mashinchi, M., Solving fuzzy number linear programming problem by lexicographic ranking function, Italian Journal of Pure and Applied Mathematics, (15) (2004) 9-20.

Ojha, A.K., and Das, A.K., Geometric programming problem with coefficients and exponents associated with binary numbers, International Journal of Computer Science Issues, (7) (2010) 49-55.

Ojha, A.K., and Biswal, K.K., Multi-objective geometric programming problem with constraint method, Applied Mathematical Modelling, (38) (2014) 747-758.

Orlovskii, S.A., Multiobjective programming problems with fuzzy parameters, Control and Cybernetics, (13) (3) (1984) 175-183.

Palomar, D., Cio, J., and Lagunas, M., Joint TX-RX beam forming design for multi-carrier MIMO channels: a unified framework for convex optimization, IEEE Trans Signal Process, (51) (9) (2003) 2381-2401.

Rajgopal, J., and Bricker, D.L., Solving Posynomial geometric programming problems via generalized linear programming, Computational Optimization and Applications, (21) (2002) 95-109.

Sahidul, I., Multi-objective marketing planning inventory model: A geometric programming approach, Applied Mathematics and Computation, (205) (2008) 238-246.

Sakawa, M., Genetic Algorithms for fuzzy multiobjective optimization, Kluwer Academic Publisher, Norwell, MA, USA, 2001.

Sen, Sh., and Pal, B.B., Interval goal programming approach to multiobjective fuzzy goal programming problem with interval weights, Procedia Technology, (10) (2013) 587-595.

Veeramani, C., and Sumathi, M., Solving Linear Fractional Programming Problem under Fuzzy Environment: Numerical Approach, Applied Mathematical Modelling, (40) (2016) 6148-6164.

Wu, Y.K., Optimizing the geometric programming problem with single-term exponents subject to max-min fuzzy relational equation constraint, Mathematical and Computer Modelling, (47) (2008) 352-362.

Xu, G., Global optimization of signomial geometric programming problems, European Journal of Operational Research, (233) (2014) 500-510.

Yang, H., and Cao, B.Y., Fuzzy geometric programming and its application, Fuzzy Information and Engineering, (2) (1) (2010) 101-112.

Yiming, L., and Chen, Y.C., Temperature-aware floor planning via geometric programming, Mathematical and Computer Modelling, (51) (2010) 927-934.

Zadeh, L.A., Fuzzy sets as a basis for a theory of possibility, Fuzzy Sets and Systems, (1) (1978) 328.

Zimmermann, H.J., Fuzzy Set Theory and its Applications, Kluwer-Nijhof, Boston, 1996.

Downloads

Published

2019-05-01

Issue

Section

Research Articles