Here‎, ‎we aim to develop a new algorithm for solving a multiobjective linear programming problem‎. ‎The algorithm is to obtain a solution which approximately meets the decision maker's preferences‎. ‎It is proved that the proposed algorithm always converges to a weak efficient solution and at times converges to an efficient solution‎. ‎Numerical examples and a simulation study are used to illu...

In the real-world optimization problems, coefficients of the objective function are not known precisely and can be interpreted as fuzzy numbers. In this paper we define the concepts of optimality for linear programming problems with fuzzy parameters (FLP). Then by using the concept of comparison of fuzzy numbers we transform FLP problem into a multiobjective linear programming (MOLP) problem. T...

In multiobjective optimisation, one of the most common ways of describing the decision makerÕs preferences is to assign targeted values (goals) to con ̄icting objectives as well as relative weights and priority levels for attaining the goals. In linear and convex decision situations, traditional goal programming provides a pragmatic and ̄exible manner to cater for the above preferences. In certa...

This thesis proposes a new necessary condition for the infeasibility of non-linear optimization problems (that becomes necessary under convexity assumption) which is stated as a Pareto-criticality condition of an auxiliary multiobjective optimization problem. This condition can be evaluated, in a given problem, using multiobjective optimization algorithms, in a search that either leads to a fea...

Financial portfolio optimization is a challenging problem. First, the problem is multiobjective (i.e.: minimize risk and maximize profit) and the objective functions are often multimodal and non smooth (e.g.: value at risk). Second, managers have often to face real-world constraints, which are typically non-linear. Hence, conventional optimization techniques, such as quadratic programming, cann...