In this paper, we analyse the multiobjective problem generated byapplying a goal programming approach to deal with linearassignment type problem. We specify sufficient conditions for asolution to be efficient for this problem. The notion ofefficiency with respect to a neighborhood is also introduced andcharacterized through sufficient conditions. Unfortunately, theseconditions are not necessary in general.

The p-principal points of a random variable X with finitesecond momentare those ppoints in ${\mathbb R}$ minimizing the expected squared distance from X tothe closest point.Although the determination of principal points involves in general theresolution of a multiextremal optimization problem, existing procedures inthe literature provide just a local optimum. In this paper we show thatstandard Global Optimization techniques can be applied.

We present a review of the main “global optimization" methods. The paper comprises one introduction and two parts. In the introduction, we recall some generalities about non linear constraint-less optimization and we list some classifications which have been proposed for the global optimization methods. We then describe, in the first part, various “classical" global optimization methods, most of which available long before the appearance of Simulated Annealing (a key event in this field). There exists plenty of papers and books dealing with these methods, and studying in particular their convergence properties. The second part of the paper is devoted to more recent or atypical methods, mostly issued from combinatorial optimization. The three main methods are “metaheuristics": Simulated Annealing (and derived techniques), Tabu Search and Genetic Algorithms; we also describe three other less known methods. For these methods, theoretical studies of convergence are less abundant in the literature, and the use of convergence results is by far more limited in practice. However, the fitting of some of these techniques to continuous variables problems gave very promising results; that question is not discussed in detail in the paper, but useful references allowing to deepen the subject are given.

We propose here a pricing Model which is an extension of the Cooperative Game concept and which includes a notion of Elastic Demand. We present some existence results as well as some algorithms. We conclude by discussing this model in the context of some Production and Transportation problems.