We study objective Bayesian inference for linear regression models with residual errors distributed according to the class of two-piece scale mixtures of normal distributions. These models allow for capturing departures from the usual assumption of normality of the errors in terms of heavy tails, asymmetry, and certain types of heteroscedasticity. We propose a general noninformative, scale-inva...

We consider the linear contextual bandit problem with global convex constraints and a concaveobjective function. In each round, the outcome of pulling an arm is a vector, that depends linearly onthe context of that arm. The global constraints require the average of these vectors to lie in a certainconvex set. The objective is a concave function of this average vector. This probl...

The traditional frameworks for fuzzy linear optimization problems are inspired by the max–min model proposed by Zimmermann using the Bellman–Zadeh extension principle to aggregate all the fuzzy sets representing flexible (fuzzy) constraints and objective functions together. In this paper, we propose an alternative approach to model fuzzy multi-objective linear programming problems (FMOLPPs) fro...

ABSTRACT The phenomenon consisting in all the feasible region of a multiple objective linear programming problem being weakly efficient, may be less unlikely than has been believed, particularly for problems whose feasible regions have no interior. To deal with such possibility of complete weak efficiency, we would want to have at our disposal proficient methods to check this situation. So, in ...

It is important to program properly during process of oilfield development. Based on the fuzzy understanding about the reservoir and the uncertainty in the actual environment, a kind of fuzzy multi-objective optimization model of production distribution was proposed in this paper, which considers the objectives of production maximization, cost minimization and profit maximization. By using the ...

This paper gives a brief introduction into multiple objective linear programming and discusses its possibilities in the area of forest management. We overview basic concepts, formulations, and principles of solving multiple objective linear programming problems. As such problems s always require the intervention of a decision-maker behavioral aspectsare also considered. We use an application by...

We interpret fuzzy multiple objective linear programming (FMOLP) problems with fuzzy coefficients and fuzzy inequality relations as multiple fuzzy reasoning schemes (MFR), where the antecedents of the scheme correspond to the constraints of the FMOLP problem and the facts of the scheme are the objectives of the FMOLP problem. Then the solution process consists of two steps: first, for every dec...

In this paper we examine multi-objective linear programming problems in the face of data uncertainty both in the objective function and the constraints. First, we derive a formula for radius of robust feasibility guaranteeing constraint feasibility for all possible uncertainties within a specified uncertainty set under affine data parametrization. We then present a complete characterization of ...

In this paper, a multi-objective approach is applied to the design of aperiodic linear arrays of antennas. The adopted procedure is based on a standard Matlab implementation of the Controlled Elitist Non-Dominated Sorting Genetic Algorithm II. Broadside symmetrical arrays of isotropic radiators are considered with both uniform and non-uniform excitations. The work focuses on whether, and in whi...

In this paper, we improve the fuzzy compromise approach of Guu and Wu by automatically computing proper membership thresholds instead of choosing them. Indeed, in practice, choosing membership thresholds arbitrarily may result in an infeasible optimization problem. Although we can adjust minimum satisfaction degree to get fuzzy efficient solution, it sometimes makes the process of interactionmo...