We consider the problem of identifying the Pareto front for multiple objectives from a finite set of operating points. Sampling an operating point gives a random vector where each coordinate corresponds to the value of one of the objectives. The Pareto front is the set of operating points that are not dominated by any other operating point in respect to all objectives (considering the mean of t...

A set of agents with different waiting costs have to receive a service of equal length of time from a single provider which can serve only one agent at a time. One needs to form a queue and set up monetary transfers to compensate the agents who have to wait. We analyze rules that are efficient, fair, and immune to strategic behavior. We prove that no rule is Pareto-efficient and coalitional str...

A new game theoretical solution concept for open spectrum sharing in cognitive radio (CR) environments is presented – the Lorenz equilibrium (LE). Both Nash and Pareto solution concepts have limitations when applied to real world problems. Nash equilibrium (NE) rarely ensures maximal payoff and it is frequently Pareto inefficient. The Pareto set is usually a large set of solutions, often too ha...

The coupling of performance functions due to common design variables and uncertainties in an engineering design process will result in difficulties in optimization design problems, such as poor collaboration among design objectives and poor resolution of design conflicts. To handle these problems, a fuzzy interactive multi-objective optimization model is developed based on Pareto solutions, whe...

In this paper we introduce and study enhanced notions of relative Pareto minimizers to constrained multiobjective problems that are defined via several kinds of relative interiors of ordering cones and occupy intermediate positions between the classical notions of Pareto and weak Pareto efficiency /minimality. Using advanced tools of variational analysis and generalized differentiation, we esta...

Previous research on the validity and adverse impact (AI) of predictor composite formation focused on the merits of regression-based or ad hoc composites. We argue for a broader focus. Ad hoc chosen composites are usually not Pareto-optimal, whereas the regression-based composite represents only one element from the total set of Pareto-optimal composites and can, therefore, provide only limited...