This paper addresses an important problem in the field of livestock ration formulation: the rigidity of the right-hand sides of the model. Thus, instead of considering the nutritional requirements as fixed values they...
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This paper addresses an important problem in the field of livestock ration formulation: the rigidity of the right-hand sides of the model. Thus, instead of considering the nutritional requirements as fixed values they are considered as targets which may or may not be achieved. In this way a multigoal programming model is formulated. The preferences of the ration-formulator are elicited resorting to the interactive method proposed by Zionts and Wallenius. The approach proposed is applied to a ration formulation problem for dairy cows in the Pedroches Valley in Andalusia, Spain.
In this paper we suggest an approach for solving a multiobjective stochastic linear programming problem with normal multivariate distributions. Our approach is a combination between a multiobjective method and a nonco...
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In this paper we suggest an approach for solving a multiobjective stochastic linear programming problem with normal multivariate distributions. Our approach is a combination between a multiobjective method and a nonconvex technique. The problem is first transformed into a deterministic multiobjective problem introducing the expected value criterion and an utility function that represents the decision makers preferences. The obtained problem is reduced to a mono-objective quadratic problem using a weighting method. This last problem is solved by DC (Difference of Convex) programming and DC algorithm. A numerical example is included for illustration.
This paper presents a procedure for solving a multiobjective chance-constrained programming problem. Random variables appearing on both sides of the chance constraint are considered as discrete random variables with a...
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This paper presents a procedure for solving a multiobjective chance-constrained programming problem. Random variables appearing on both sides of the chance constraint are considered as discrete random variables with a known probability distribution. The literature does not contain any deterministic equivalent for solving this type of problem. Therefore, classical multiobjective programming techniques are not directly applicable. In this paper, we use a stochastic simulation technique to handle randomness in chance constraints. A fuzzy goal programming formulation is developed by using a stochastic simulation-based genetic algorithm. The most satisfactory solution is obtained from the highest membership value of each of the membership goals. Two numerical examples demonstrate the feasibility of the proposed approach.
A pair of multiobjective mixed symmetric dual programs is formulated over arbitrary cones. Weak, strong, converse and self-duality theorems are proved for these programs under K-preinvexity and K-pseudoinvexity assump...
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A pair of multiobjective mixed symmetric dual programs is formulated over arbitrary cones. Weak, strong, converse and self-duality theorems are proved for these programs under K-preinvexity and K-pseudoinvexity assumptions. This mixed symmetric dual formulation unifies the symmetric dual formulations of Suneja et al. (2002) [14] and Khurana (2005) [15]. (C) 2009 Elsevier Ltd. All rights reserved.
We are concerned with nonlinear multiobjective programming problem with inequality constraints. We introduce some classes of nonconvex. functions by relaxing the definitions of invex and preinvex functions. Examples a...
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We are concerned with nonlinear multiobjective programming problem with inequality constraints. We introduce some classes of nonconvex. functions by relaxing the definitions of invex and preinvex functions. Examples are given to shows relationship between them. Various first and second order sufficient optimality theorems are obtained. Moreover, we prove duality and converse duality results for Mond-Weir type dual.
In the restructured electricity industry, the engineering aspects of planning need to be reformulated even though the goal to attain remains substantially the same, requiring various objectives to be simultaneously ac...
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In the restructured electricity industry, the engineering aspects of planning need to be reformulated even though the goal to attain remains substantially the same, requiring various objectives to be simultaneously accomplished to achieve the optimality of the power system development and operation. In many cases, these objectives contradict each other and cannot be handled by conventional single optimization techniques. In this paper, a multiobjective formulation for the siting and sizing of DG resources into existing distribution networks is proposed. The methodology adopted permits the planner to decide the best compromise between cost of network upgrading, cost of power losses, cost of energy not supplied, and cost of energy required by the served customers. The implemented technique is based on a genetic algorithm and an E-constrained method that allows obtaining a set of noninferior solutions. Application examples are presented to demonstrate the effectiveness of the proposed procedure.
In this paper, a new approximation method is introduced to characterize a so-called vector strict global minimizer of order 2 for a class of nonlinear differentiable multiobjective programming problems with (F, rho)-c...
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In this paper, a new approximation method is introduced to characterize a so-called vector strict global minimizer of order 2 for a class of nonlinear differentiable multiobjective programming problems with (F, rho)-convex functions of order 2. In this method, an equivalent vector optimization problem is constructed by a modification of both the objectives and the constraint functions in the original multiobjective programming problem at the given feasible point. In order to prove the equivalence between the original multiobjective programming problem and its associated F-approximated vector optimization problem, the suitable (F, rho)-convexity of order 2 assumption is imposed on the functions constituting the considered vector optimization problem. (C) 2011 Elsevier B.V. All rights reserved.
Energy policies and technological progress in the development of wind turbines have made wind power the fastest growing renewable power source worldwide. The inherent variability of this resource requires special atte...
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Energy policies and technological progress in the development of wind turbines have made wind power the fastest growing renewable power source worldwide. The inherent variability of this resource requires special attention when analyzing the impacts of high penetration on the distribution network. A time-series steady-state analysis is proposed that assesses technical issues such as energy export, losses, and short-circuit levels. A multiobjective programming approach based on the nondominated sorting genetic algorithm (NSGA) is applied in order to find configurations that maximize the integration of distributed wind power generation (DWPG) while satisfying voltage and thermal limits. The approach has been applied to a medium voltage distribution network considering hourly demand and wind profiles for part of the U.K. The Pareto optimal solutions obtained highlight the drawbacks of using a single demand and generation scenario, and indicate the importance of appropriate substation voltage settings for maximizing the connection of MPG.
In this paper, a pair of second-order multiobjective mixed symmetric dual programs over arbitrary cones is formulated. The usual duality results are then established for the aforementioned pair using the notion of eta...
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In this paper, a pair of second-order multiobjective mixed symmetric dual programs over arbitrary cones is formulated. The usual duality results are then established for the aforementioned pair using the notion of eta-bonvexity/eta-pseudobonvexity assumptions. (C) 2011 Elsevier Ltd. All rights reserved.
In this paper, the so-called eta-approximation approach is used to characterize solvability (in Pareto sense) of a nonlinear multiobjective programming problem with G-invex functions with respect to the same function ...
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In this paper, the so-called eta-approximation approach is used to characterize solvability (in Pareto sense) of a nonlinear multiobjective programming problem with G-invex functions with respect to the same function eta. In this method, an equivalent eta-approximated vector optimization problem is constructed by a modification of both the objective and the constraint functions in the original multiobjective programming problem at the given feasible point. Moreover, in order to find a (weak) Pareto optimal solution in the original multiobjective problem, it is sufficient to solve its associated eta-approximated vector optimization problem equivalent to the original multiobjective programming problem in the sense discussed in this paper. (C) 2011 Published by Elsevier Ltd
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