The Binary Integer Programming problem (BIP) is a mathematical optimization problem, with linear objective function and constraints, on which the domain of all variables {0, 1}. In BIP, every variable associated determined cost coefficient. Minmax regret under interval uncertainty (M-BIP) generalization BIP in coefficient to not known advance, but are assumed be bounded by an interval. M-BIP fi...

In Game theory, there are situations in which it is very difficult to characterize the private information of each player. In this case, the payoffs can be given by approximate values, represented by fuzzy numbers. Whenever there is uncertainty in the modeling of those fuzzy numbers, interval fuzzy numbers may be used. This paper introduces two approaches for the solution of interval-valued fuz...

For optimization or decision-making problems with interval uncertainty, the interval comparison relation plays a very important role, as only based on it a better or best decision can be determined. In this paper, a new kind of interval comparison relation termed as reliability-based possibility degree of interval is proposed to give quantitative evaluations on “how much better” of one interval...

Data envelopment analysis (DEA) is an effective tool for supporting decision-makers to assess bankruptcy, uncertainty concepts including intervals, and game theory. The bankruptcy problem with the qualitative parameters is an economic problem under uncertainty. Accordingly, we combine the concepts of the DEA game theory and uncertain models as interval linear programming (ILP), which can be app...

Matrix game theory is concerned with how two players make decisions when they are faced with known exact payoffs. The aim of this paper is to develop a simple and an effective linear programming method for solving matrix games in which the payoffs are expressed with intervals. Because the payoffs of the matrix game are intervals, the value of the matrix game is an interval as well. Based on the...

In this paper, we consider a kind of bilevel linear programming problem where the coefficients of both objective functions are fuzzy random variables. The purpose of this paper is to develop a computational method for obtaining optimistic Stackelberg solutions to such a problem. Based on a level sets of fuzzy random variables, we first transform the fuzzy random bilevel programming problem into...

Support vector machine (SVM) has been very successful in pattern recognition and function estimation problems for crisp data. This paper proposes a new method to evaluate interval linear and nonlinear regression models combining the possibility and necessity estimation formulation with the principle of SVM. For data sets with crisp inputs and interval outputs, the possibility and necessity mode...

This paper tackles the real-time Railway Traffic Management Problem (rtRTMP). It is problem of finding optimal choices for train schedules and routes to minimize delays due conflicts. We present a new Constraint Programming (CP) algorithm rtRTMP. The formulation at basis this exploits concept conditional time-interval variables provided in CP Optimizer library. A variable assumes value represen...

Profit maximization is an important issue to the firms that pursue the largest economic profit possible. In this paper, we consider the profit-maximization problem with the known CobbDouglas production function. Its equivalent geometric programming form is given. Then due to the presence of uncertainties in real world modeling, we have assumed interval uncertainties on the model parameters. The...