FLIP - Multiobjective Fuzzy Linear Programming Package

FLIP (Fuzzy LInear Programming) is a package designed to help in analysis of multiobjective linear programming (MOLP) problems in an uncertain environment. The uncertainty of data is modeled by L-R type fuzzy numbers. They can appear in the objective functions as well as on the both sides of the constraints. The input data to the FLIP package include the characteristics of the analyzed fuzzy MOLP problem, i.e., the number of criteria, constraints and decision variables, fuzzy cost coefficients for every objective and fuzzy coefficients of LHS and RHS for all constraints. The data loading is supported by a graphical presentation of fuzzy coefficients. The calculation is preceded by a transformation of the fuzzy MOLP problem into a multiobjective linear fractional program. It is then solved with an interactive method using a linear programming procedure as the only optimiser. In every iteration, one gets a series of solutions that are presented very clearly in a graphical and numerical form. In FLIP, interaction with the user takes place at two levels: first, when safety parameters have to be defined in the transformation phase, and second, when the associate deterministic problem is solved. The package is written in TURBO-Pascal and can be used on microcomputers compatible with IBM-PC XT/AT with hard disc and a graphic card

Stochastic Approach versus Multiobjective Approach for Obtaining Efficient Solutions in Stochastic Multiobjective Programming Problems

In this work, we deal with obtaining efficient solutions for stochastic multiobjective programming problems. In general, these solutions are obtained in two stages: in one of them, the stochastic problem is transformed into its equivalent deterministic problem, and in the other one, some of the existing generating techniques in multiobjective programming are applied to obtain efficient solutions, which involves transforming the multiobjective problem into a problem with only one objective function. Our aim is to determine whether the order in which these two transformations are carried out influences, in any way, the efficient solution obtained. Our results show that depending on the type of stochastic criterion followed and the statistical characteristics of the initial problem, the order can have an influence on the final set of efficient solutions obtained for a given problem.Stochastic Multiobjective Programming, Efficiency, Stochastic Approach, Multiobjective Approach.

Ideal points in multiobjective programming

The main object of this paper is to give conditions under which a minimal solution to a problem of mathematical programming can be transformed into a minimum solution in the usual sense of the order relations, or in every case, conditions under which that solution is adherent to the set of the points wich verify this last property. The interest of this problem is clear, since many of the usual properties in optimization (like, for instance, the analysis of the sensitivity of the solutions) are studied more easily for minimum solutions than for minimal solutions

An Interactive Fuzzy Satisficing Method for Fuzzy Random Multiobjective 0-1 Programming Problems through Probability Maximization Using Possibility

In this paper, we focus on multiobjective 0-1 programming problems under the situation where stochastic uncertainty and vagueness exist at the same time. We formulate them as fuzzy random multiobjective 0-1 programming problems where coefficients of objective functions are fuzzy random variables. For the formulated problem, we propose an interactive fuzzy satisficing method through probability maximization using of possibility

An application of multiobjetive programming to the study of workers' satisfaction in the spanish labour market

In this paper, a multiobjective scheme is used to study the satisfaction levels of the Spanish workers. Data obtained from a panel survey conducted in several European countries are used to build up a multiobjective model, on the basis of a previous statistical and econometric analysis of these data. Then, a Reference Point based method is implemented to determine the profile of the most satisfied worker in Spain nowadays. Finally, a combined Goal Programming – Reference Point approach is used to determine policies than can be carried out in order to increase the workers’ satisfaction levels.Workers’ Satisfaction, Econometric analyses, Multiobjective Programming.