On solving possibilistic multi- objective De Novo linear programming
محورهای موضوعی : Operation Research
1 - Operations Research- ISSR- Cairo University
کلید واژه: linear programming, Multi-objective, De Novo programming, Possibilistic variables, possibily efficient solution, Trade-offs,
چکیده مقاله :
Multi-objective De Novo linear programming (MODNLP) is problem for designing optimal system by reshaping the feasible set (Fiala [3] ). This paper deals with MODNLP having possibilistic objective functions coefficients. The problem is considered by inserting possibilistic data in the objective functions coefficients. The solution of the problem is defined and established under the using of efficient and necessary condition. Also, the relation between possibilistic levels corresponding to the solution is constructed. A solution procedure for solving the problem is proposed. A numerical example is given for illustration.
Babic, Z., Veza, I., Balic, A., & Crnjac, M. (2018). Application of De Novo Programming Approach for Optimizing the Business Process. World Academy of Science, Engineering and Technology, International Journal of Industrial and Systems Engineering, 12(5), 519-524.
Chen, J. K., & Tzeng, G. H. (2009). Perspective strategic alliances and resource allocation in supply chain systems through the De Novo programming approach. International journal of sustainable strategic management, 1(3), 320-339.
Eren, M. (2017). A two-phase procedure for a multi-objective programming problem with fuzzy coefficients based on group decision-making for project selection. Croatian Operational Research Review, 8(1), 317-333.
Fiala, P. (2011). Multiobjective De Novo Linear Programming. Acta universitatis palackianae olomucensis. Facultas rerum naturalium. Mathematica, 50(2), 29-36.
Garg, H. (2018). Analysis of an industrial system under uncertain environment by using different types of fuzzy numbers. International journal of system assurance engineering and management, 9(2), 525-538.
Hamadameen, O. A. (2018). A noval technique for solving multi- objective linear programming problems. Aro- The Scientific Journal of Koya University, 5(2), 1-8.
Huang, J. J., Tzeng, G. H., & Ong, C. S. (2006). Choosing best alliance partners and allocating optimal alliance resources using the fuzzy multi-objective dummy programming model. Journal of the operational research society, 57(10), 1216-1223.
Hussein, M. L. (1998). Complete solutions of multiple objective transportation problems with possibilistic coefficients. Fuzzy Sets and Systems, 93(3), 293-299.
Khalifa, H. A. (2018). On Solutions of Fuzzy Multi-Objective Programming Problems through Weighted Coefficients in Two-Phase Approach. J. Appl. Res. Ind. Eng. Vol, 5(2), 110-1218.
Khalifa, H. A. (2018). On solving fully fuzzy multi-criteria De Novo programming via fuzzy goal programming approach. Journal of Applied Research on Industrial Engineering, 5(3), 239-252.
Kiruthiga, M., & Loganathan, C. (2015). Fuzzy multi-objective linear programming problem using membership function. International journal of science, engineering, and technology, applied sciences, 5(8), 1171-1178.
Li, R. J., & Lee, E. S. (1990). Fuzzy approaches to multicriteria De Novo programs. Journal of mathematical analysis and applications, 153(1), 97-111.
Li, R. J., & Lee, E. S. (1990). Multi-criteria de novo programming with fuzzy parameters.Computers & Mathematics with Applications, 19(5), 13-20.
Luhandjula, M. K. (1986). On possibilistic linear programming. Fuzzy Sets and Systems, 18(1), 15-30.
Luhandjula, M. K. (1987). Multiple objective programming problems with possibilistic coefficients. Fuzzy sets and systems, 21(2), 135-145.
Sakawa, M., & Yano, H. (1989). Interactive decision making for multiobjective nonlinear programming problems with fuzzy parameters. Fuzzy sets and Systems, 29(3), 315-326.
Shi, Y. (1995). Studies on optimum-path ratios in multicriteria De Novo programming problems. Computers & Mathematics with Applications, 29(5), 43-50
Tabucanon, M. T. (1988). Multiple criteria decision making in industry (Vol. 8). Elsevier Science Ltd.
Umarksman, O.U.N. (2019). Using global criterion method to define priorities in Lexicographic goal programming and an application for optimal system design. MANAS Journal of Social Studies, 8(1/1): 333- 348.
Umarusman, N., & Turkmen, A. (2013). Building optimum production settings using de novo programming with global criterion method. International journal of computer applications, 82(18).
Zeleny, M., & Raff, S. (1980). Mathematical programming with multiple objectives. Compuf. Ops Res, 7(1-2)
Zeleny, M. (1986). Optimal system design with multiple criteria: De Novo programming approach. Engineering costs and production economics, 10(1), 89-94.
Zeleny, M. (1990). Optimizing given systems vs. designing optimal systems: The De Novo programming approach. International journal of general systems, 17(4), 295-307.
Zeleny, M. (2010). Multiobjective optimization, systems design and De Novo programming. In Handbook of Multicriteria Analysis (pp. 243-262). Springer, Berlin, Heidelberg.
Zhang, Y. M., Huang, G. H., & Zhang, X. D. (2009). Inexact de Novo programming for water resources systems planning. European journal of operational research, 199(2), 531-541.
Zhuang, Z. Y., & Hocine, A. (2018). Meta goal programing approach for solving multi-criteria de Novo programing problem. European journal of operational research, 265(1), 228-238.
