In this paper, we suggested two new techniques for transforming multi-objective linear fractional programming problems into single-objective linear fractional programming problems (SOLFPP) and then solving them by methods of SOLFPP. Illustrative numerical examples are presented for demonstration purposes. Also, the obtained results are compared with another method studied previously.
In this paper, we suggested a new technique by using optimal geometric average for the value of functions, to solve extreme point multi- objective quadratic programming problem (EPMOQPP), via transforming it to extreme point single-objective quadratic programming problem(EPSOQPP), then solve the problem by Wolfe’s method [1] , and an algorithm is given for its solution, also using cutting plane technique when the optimal value of the objective function is not an extreme point of constraints, the computer application for this algorithm was tested on a number of numerical examples, which are also solved by several techniques (Chandra Sen.'s, mean, median, and optimal arithmetic). After comparing, the results indicate that the new technique is best than others as shown in table (3).
Multi-objective optimization also known as multi-objective programming is an area of multiple criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously. In such circumstance, we have to be discovered out compromise arrangement which is ideal for all the objectives in a few senses. In this paper, we transformed multi-objective linear plus linear fractional programming problems to single QPP and then solved by methods of QPP. Illustrative numerical examples are displayed for exhibit reason. We have explored an arrangement to the MOLPLFP issue based on a hypothesis already considered by Dinkelbach. He clearly delineated a calculation for fractional programming with nonlinear as well as linear terms within numerator and denominator.
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