Optimality and mixed duality in multiobjective E-convex programming

In this paper, we present a multi-objective fractional probabilistic programming problem where the parameters in the objective functions and right-hand side constraints are multi-choice in nature. Multi-choice multi-objective objective fractional probabilistic programming problem is formulated. The multi-choice parameters in the right-hand side of constraints are random variables following Cauchy distribution. Multi-choice multi-objective fractional probabilistic programming problem is transformed to deterministic equivalent multi-choice multi-objective fractional programming problem using chance-constrained programming problem. Lagrange interpolating polynomial approach is proposed to deal with multi-choice parameters. The multi-choice multi-objective fractional programming problem is transformed to non-linear mixed integer multi-objective programming problem. An \(\epsilon -\) constrained method is applied to find the compromise solutions of non-linear mixed integer multi-objective programming problem using different values of \(\epsilon \). LINGO software is applied to find the optimal solution of single objective non-linear mixed integer programming problem. Finally, a numerical example is given to illustrate the proposed programming problem.KeywordsMulti-choice programming problemMulti-objective programming problemProbabilistic programming problemFractional programming problem\(\epsilon \)-constraint method

A linear production programming problem in which multiple decisionmakers have resources and produce several kinds of products in collaboration is considered. It is formulated as a problem in which the objective function is the income obtained by selling several kinds of products, and the objective is to maximize the income under the resource constraint. The problem also includes the fair allocation (imputation) of the obtained common income. This paper discusses the linear production programming problem with multiple decisionmakers in a multiobjective environment. The multicommodity game is derived from the multiobjective production programming problem, and the nonemptiness of the core of the multicommodity game is demonstrated, based on the special property of the game. It is shown that when the multiobjective production programming problem is the primal problem, payoffs belonging to the core can be calculated from the optimal solution of the dual problem. The validity of the approach is shown by a numerical example. © 1999 Scripta Technica, Electron Comm Jpn Pt 3, 82(5): 29–38, 1999

The present work defines interval and interval valued function in terms of parameters. Two types of multi-objective programming problems are considered: one is a general multi-objective interval fractional programming problem and the other is a parametric form of the first problem, where the objective and constraint functions are interval valued. Relationships between the solutions of both problems are developed, and some of the results are illustrated through numerical example.

On computational methods for solutions of multiobjective linear production programming games

Nonlinear optimization problems have two different representations, the primal problem and the dual problem. The relation between the primal and the dual problem is provided by an elegant duality theory. This chapter presents the basics of duality theory. Section 4.1 discusses the primal problem and the perturbation function. Section 4.2 presents the dual problem. Section 4.3 discusses the weak and strong duality theorems, while section 4.4 discusses the duality gap. This section presents the formulation of the primal problem, the definition and properties of the perturbation function, the definition of stable primal problem, and the existence conditions of optimal multiplier vectors.

In this paper, we have considered a nonsmooth multiobjective optimization problem where the objective and constraint functions involved are directionally differentiable. A new class of generalized functions (d − ρ − η − θ)-type I univex is introduced which generalizes many earlier classes cited in literature. Based upon these generalized functions, we have derived weak, strong, converse and strict converse duality theorems for mixed type multiobjective dual program in order to relate the efficient and weak efficient solutions of primal and dual problem.

Necessary and sufficient saddle-point- and stationary-point-type proper efficiency conditions are established for a class of continuous-time multiobjective fractional programming problems with convex operator inequality and affine operator equality constraints. Subsequently, the form and contents of these proper efficiency results are utilized for constracting a Lagrangian-type, two parametric, and four semiparametric duality models, and for proving appropriate duality theorems. These proper efficiency and duality results contain, as special cases, similar results for continuous-time programming problems with nonfractional multiple, single fractional, and conventional objective functions, which are patricular cases of the principal problem studied in this paper.

In this paper, we focus on multiobjective two-level fuzzy random programming problems with simple recourses, in which multiple objective functions are involved in each level, shortages and excesses resulting from the violation of the constraints with fuzzy random variables are penalized, and sums of the objective functions and expectation of the amount of the penalties are minimized. To deal with such problems, the concept of estimated Pareto Stackelberg solutions of the leader is introduced under the assumption that the leader can estimate the preference of the follower as a weighting vector of the weighting problems. Employing the possibility measure for fuzzy numbers, weighting method for multiobjective programming problems, and Kuhn-Tucker approach for two-level programming problems, a nonlinear optimization problem under complementarity conditions is formulated to obtain the estimated Pareto Stackelberg solutions for the leader. A numerical example illustrates the proposed method for a multiobjective two-level fuzzy random programming problem with simple recourses. Several types of estimated Pareto Stackelberg solutions are derived corresponding to the weighting vectors and permissible possibility levels specified by the leader.

This paper deals with non-differentiable multi-objective semi-infinite programming problem. It is a problem of simultaneous minimisation of finitely many scalar valued functions subject to an arbitrary (possibly infinite) set of constraints. Non-differentiability enters, due to the square root of a quadratic form which appears in the objective functional. Concept of efficiency of order m has been extended to the above stated problem. In order to study this new solution concept, the notion of ρ-invexity of order m is also proposed which is utilised to establish sufficient optimality conditions for the non-differentiable multi-objective semi-infinite programming problem. Mond-Weir type of dual is proposed for which weak, strong and strict converse duality theorems are established.

A neural network approach to multiobjective and multilevel programming problems

In this paper, a class of nonsmooth multiobjective programming problems is considered. We introduce the new concept of invex of order type II for nondifferentiable locally Lipschitz functions using the tools of Clarke subdifferential. The new functions are used to derive the sufficient optimality condition for a class of nonsmooth multiobjective programming problems. Utilizing the sufficient optimality conditions, weak and strong duality theorems are established for Wolfe type duality model.

This paper is intended to study higher-order necessary optimality conditions for multiobjective semi-infinite programming problems, in which the objective and constraint functions are considered to be m-stable at a reference point. Two data qualifications, called the weak Abadie data qualification (WADQ) and the strong Su–Luu data qualification (SSLDQ), are introduced in terms of mth-order upper Studniarski derivatives. The mth-order strong KKT necessary optimality conditions for a local weakly efficient solution are presented under the WADQ and the SSLDQ. Additionally, if the reference point is further a local Borwein-properly efficient solution, the mth-order strong KKT necessary optimality conditions are satisfied without requiring the assumption of SSLDQ.

In this paper, we propose a linear programming based interactive method for multiobjective linear programming problems, in which fuzzy coefficients and random variable coefficients are involved in the objective functions simultaneously. In the proposed method, it is assumed that the decision maker has a fuzzy goal for each objective function, and such a fuzzy goal can be quantified by eliciting the membership function. Through the possibility measure and a fractile optimization model, the original problem is transformed to the well-defined multiobjective programming problem. Then, a generalized Pareto optimal concept is defined, and a linear programming based interactive algorithm is proposed to obtain a satisfactory solution from among a generalized Pareto optimal solution set.

Convergence of exponential penalty function method for multiobjective fractional programming problems

Degree of difficulty is an important concept in the classicalGeometric Programming (GP) theory.The dual problem is often infeasible when the degree of difficulty is negative and very little subjects have been published on this topic.This paper presents the basic concepts and principles of multiple-objective geometric programming model, and developed a numerical procedure to solve multi-objective Geometric Programming Problems (GPP) having a negative degree of difficulty using weighted method to obtain the non-inferior solution and using Brickers simple technique to ensure the dual feasibility; namely the addition of a constant term to the primal objective function.