A New Approach for Solving Fuzzy Linear Multi-Criterion Problems: An Approach Based on Minimization of the Errors Functions
- 1 Département des Mathématiques et Informatique, Université de Kinshasa, Kinshasa, The Democratic Republic of the Congo
- 2 Département des Mathématiques et Informatique, Université de Kinshasa, Kinshasa, The Democratic Republic of the Congo
- 3 Département des Mathématiques et Informatique, Université de Kinshasa, Kinshasa, The Democratic Republic of the Congo
- 4 Département des Mathématiques et Informatique, Université de Kinshasa, Kinshasa, The Democratic Republic of the Congo
- 5 Conférence Congolaise de Recherche en Informatique et en Mathématiques Appliquées, COCORIMA, Kinshasa, The Democratic Republic of the Congo
Abstract
The main purpose of this paper is to build a new approach for solving a fuzzy linear multi-criterion problem by defining a function called “error function”. For this end, the concept of level set is used to construct the error function. In addition, we introduce the concept of deviation variable in the definition of the error function. The algorithm of the new approach is summarized in three main steps: first , we transform the original fuzzy problem into a deterministic one by choosing a specific level . second , we solve separately each uni-criteria problem and we compute the error function for each criteria. Finally, we minimize the sum of error functions in order to obtain the desired compromise solution. A numerical example is done for a comparative study with some existing approaches to show the effectiveness of the new approach.
- Mangongo, Y.T., Deffo, C.T., Fono, L.A., Bukweli, J.D.K. and Kampempe, J.D.B. (2018) Portfolio Selection with Fuzzy Return without Target Values. International Journal of Scientific Engineering and Research, 6, 18-29.
- Mangongo, Y.T., Kampempe, J.D.B. and Luhandjula, M.K. (2021) A Kaleodoscopic View of Fuzzy Stochastic Optimization. American Journal of Operations Research, 11, 283-308. https://doi.org/10.4236/ajor.2021.116018
- Zadeh, L. (1965) Fuzzy Sets. University of California, Berkeley.
- Luhandjula, M.K. (1989) Fuzzy Optimization: An Appraisal. Fuzzy Sets and Systems, 30, 257-282. https://doi.org/10.1016/0165-0114(89)90019-5
- Mangongo, Y.T. and Kampempe, J.D.B. (2021) Some Approaches for Fuzzy Multiobjective Programming Problems. Journal of Advances in Applied Mathematics, 6, 15.
- Reardon, B.J. (1997) Fuzzy Logic versus Niched Pareto Multiobjective Genetic Algorithm Optimization: Part I: Schaffer’s Problem. National Laboratory, Los Alamos.
- Rommelfanger, H. (1994) Some Problems of Fuzzy Optimization with T-norm based Extended Addition. In: Delgado, M., Kacprzyk, J., Verdegay, J.-L. and Vila, M.A., Eds., Fuzzy Optimization: Recent Advances, Physica, Heidelberg, 158-168.
- Sakawa, M. and Yano, H. (1988) An Interactive Fuzzy Satisfying Method for Multiobjective Linear Fractional Programming Problems. Fuzzy Sets and Systems, 28, 129-144. https://doi.org/10.1016/0165-0114(88)90195-9
- Tanaka, H., Ichihashi, H. and Asai, K. (1985) Fuzzy Decisions in Linear Programming with Trapezoidal Fuzzy Parameters. In: Kacprzyk, J. and Yager, R. Eds., Management Decision Support Systems Using Fuzzy Sets and Possibility Theory, Springer, Heidelberg, 146-159.
- Dubois, D. and Prade, H. (1988) Possibility Theory: An Approach to Computerized Processing of Uncertainty. Springer, New York.
- Dubois, D. and Prade, H. (1980) Fuzzy Sets and Systems: Theory and Applications. Academic Press, New-York.
- Kampempe, J.D.B. (2013) Sur la prise en compte de l’imprécision en programmation mathématique multiobjectif. Ph.D. Thesis, Université de Kinshasa, Kinshasa.