The problem of solving multi-objective linear-programming problems, by assuming that the decision maker has fuzzy goals for each of the objective functions, is addressed. Several methods have been proposed in the literature in order to obtain fuzzyefficient solutions to fuzzy multi-objective programming problems. In this paper we show that, in the case that one of our goals is fully achieved, a...

This paper deals with the detection of multiple changepoints for independent but non identically distributed observations, which are assumed to be modeled by a linear regression with normal errors. The problem has a natural formulation as a model selection problem and the main difficulty for computing model posterior probabilities is that neither the reference priors nor any form of empirical B...

A decision-maker who aims to select the ”best” collection of alternatives from the finite set of available ones might be severely restricted in the design of the selection method. If the representation of valuations of available alternatives is subject to invariance under linear scaling, such as the choice of the unit of measurement, a sensible way to compare choices is to compare weighted sums...

This paper presents an ADBASE-based parallel algorithm for solving multiple objective linear programs (MOLPs). Job balance, speedup and scalability are of primary interest in evaluating e ciency of the new algorithm. Implementation results on Intel iPSC/2 and Paragon multiprocessors show that the algorithm signi cantly speeds up the process of solving MOLPs, which is understood as generating al...

In this paper, bi-level linear programming problem with multiple objectives at each level in intuitionistic fuzzy environment is studied and solved. In this problem, technological coefficients and resources are characterized by intuitionistic fuzzy numbers having linear membership and non-membership function. Intuitionistic fuzzy efficient solution is computed for the upper level by the propose...

Consider a linear program with uncertain objective coefficients, for which we have a Bayesian prior. We can collect information to improve our understanding of these coefficients, but this may be expensive, giving us a separate problem of optimizing the collection of information to improve the quality of the solution relative to the true cost coefficients. We formulate this information collecti...