The aim of this paper is to study the possibility to obtain efficient as well as satisfying solutions when solving a general non linear convex Goal programming problem. Firstly, uniqueness conditions are given under w...
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The aim of this paper is to study the possibility to obtain efficient as well as satisfying solutions when solving a general non linear convex Goal programming problem. Firstly, uniqueness conditions are given under which we can assure that the solution obtained is efficient. In case it is not efficient, some methods to obtain them are proposed, for both cases when there exist satisfying solutions for the problem, and when they do not exist In the first case, that is, when there exist satisfying solutions, an algorithm to approximate the set of solutions which are satisfying and efficient at the same time is given. Finally, some computational results are offered, showing the behaviour of the algorithm with several test problems.
One of the fascinating things about multiple criteria decision making (MCDM) is the degree to which the contributions that have built the field have come from all over the world. In this paper the international nature...
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An equivalence is demonstrated between solving a linear complementarity problem with general data and finding a certain subset of the efficient points of a multiple objective programming problem. A new multiple object...
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An equivalence is demonstrated between solving a linear complementarity problem with general data and finding a certain subset of the efficient points of a multiple objective programming problem. A new multiple objective programming based approach to solving linear complementarity problems is presented. Results on existence, uniqueness and computational complexity are included.
We show that the Cottle-Dantzig generalized linear complementarity problem (GLCP) is equivalent to a nonlinear complementarity problem (NLCP), a piecewise linear system of equations (PLS), a multipleobjective program...
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We show that the Cottle-Dantzig generalized linear complementarity problem (GLCP) is equivalent to a nonlinear complementarity problem (NLCP), a piecewise linear system of equations (PLS), a multiple objective programming problem (MOP), and a variational inequalities problem (VIP). On the basis of these equivalences, we provide an algorithm for solving problem GLCP.
In this paper we present a procedure for solving multiple criteria decision making problems. Our procedure, which is an extension of the Analytic Hierarchy Process, addresses the issue of fuzziness in the weighing inf...
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In this paper we present a procedure for solving multiple criteria decision making problems. Our procedure, which is an extension of the Analytic Hierarchy Process, addresses the issue of fuzziness in the weighing information provided by the decision maker, the consequent stability of the AHP ranking of the alternatives and the sensitivity of such rankings to small perturbations. These issues are especially important when the concept of optimality incorporates parameters or weights that reflect subjective preferences. The procedure provides a consistent and systematic method for carrying out goal-seeking sensitivity analysis that captures the decision maker's preference structure using his/her indifference region.
Recently the author has given a duality theorem in multiple objective programming relating properly efficient solutions of the primal and dual problems. In this note the converse of that theorem is given showing that,...
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Recently the author has given a duality theorem in multiple objective programming relating properly efficient solutions of the primal and dual problems. In this note the converse of that theorem is given showing that, under certain assumptions, a properly efficient solution of the dual is also a properly efficient solution of the primal.
This article describes a multiobjectiveprogramming (MOP) framework for integrating timber and wildlife management. The framework allows for the simultaneous consideration of timber and wildlife objectives. Management...
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This article describes a multiobjectiveprogramming (MOP) framework for integrating timber and wildlife management. The framework allows for the simultaneous consideration of timber and wildlife objectives. Management strategies are defined in terms of management regimes consisting of a time-identified and site-specific schedule of activities. A MOP model is described and demonstrated using an integrated planning example involving a forest managed for timber production and a variety of wildlife species.
The Analytic Hierarchy Process is a useful tool for multiobjective decision making in its own right. In addition, it has the potential for expediting multiple objective programming analyses. multipleobjective program...
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The Analytic Hierarchy Process is a useful tool for multiobjective decision making in its own right. In addition, it has the potential for expediting multiple objective programming analyses. multiple objective programming techniques face the problem of a large (if not infinite) number of alternatives. Generally, the problem involves optimizing some unexpressed utility function over a feasible region. AHP provides a useful means of obtaining an initial linear approximation of this unexpressed utility function, with the potential of expediting the MOP analysis. Other benefits include enhancing decision maker learning through use of the consistency measure. Problems discussed are the impact of comparing many objectives, the sensitivity of the consistency index, and the use of the eigen vector as a literal estimator of utility.
Single objective cost minimization linear programming models are used as computerized decision-aids in sausage manufacturing (hot dogs, bologna, salami, etc.). However, sausage blending is clearly a problem with multi...
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Single objective cost minimization linear programming models are used as computerized decision-aids in sausage manufacturing (hot dogs, bologna, salami, etc.). However, sausage blending is clearly a problem with multiple conflicting criteria (cost, color, fat, protein, moisture, etc.) Presented in this paper is a vector-maximum/filtering MOLP (multipleobjective linear programming) methodology for use as an improved decision-making approach with single formula sausage blending problems.
The procedure samples the efficient set by computing the nondominated criterion vector that is closest to an ideal criterion vector according to a randomly weighted Tchebycheff metric. Using ‘filtering’ techniques, ...
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The procedure samples the efficient set by computing the nondominated criterion vector that is closest to an ideal criterion vector according to a randomly weighted Tchebycheff metric. Using ‘filtering’ techniques, maximally dispersed representatives of smaller and smaller subsets of the set of nondominated criterion vectors are presented at each iteration. The procedure has the advantage that it can converge to non-extreme final solutions. Especially suitable for multipleobjective linear programming, the procedure is also applicable to integer and nonlinear multipleobjective programs.
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