Multi-objective Nonlinear Programming Research Articles

Extensions of TOPSIS for large scale multi-objective non-linear programming problems with block angular structure

This paper focuses on multi-objective large-scale non-linear programming (MOLSNLP) problems with block angular structure. We extend the technique for order preference by similarity ideal solution (TOPSIS) to solve them. Compromise (TOPSIS) control minimizes the measure of distance, provided that the closest solution should have the shortest distance from the positive ideal solution (PIS) as well as the longest distance from the negative ideal solution (NIS). As the measure of “closeness” L P -metric is used. Thus, we reduce a q -dimensional objective space to a two-dimensional space by a first-order compromise procedure. The concept of a membership function of fuzzy set theory is used to represent the satisfaction level for both criteria. Moreover, we derive a single objective large-scale non-linear programming (LSNLP) problem using the max–min operator for the second-order compromise operation. Finally, a numerical illustrative example is given to clarify the main results developed in this paper.

Second Order Mixed Symmetric Duality in Non-Differentiable Multi-Objective Mathematical Programming

A pair of Mond-Weir type second order mixed symmetric duals is presented for a class of non-differentiable multi-objective non-linear programming problems with multiple arguments. We establish duality theorems for the new pair of dual models under second order generalized convexity assumptions. This mixed second order dual formulation unifies the two existing second order symmetric dual formulations in the literature. Many recent works on symmetric duality are obtained as special cases of the results established in the present paper.

AN η-APPROXIMATION APPROACH IN NONLINEAR VECTOR OPTIMIZATION WITH UNIVEX FUNCTIONS

In this paper, the so-called η-approximation approach is used to obtain the sufficient conditions for a nonlinear multiobjective programming problem with univex functions with respect to the same function η. In this method, an equivalent η-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, to find the optimal solutions of the original multiobjective problem, it sufficies to solve its associated η-approximated vector optimization problem. Finally, the description of the η-approximation algorithm for solving a nonlinear multiobjective programming problem involving univex functions is presented.

A probabilistic bi-level linear multi-objective programming problem to supply chain planning

Bi-level programming, a tool for modeling decentralized decisions, consists of the objective(s) of the leader at its first level and that is of the follower at the second level. Three level programming results when second level is itself a bi-level programming. By extending this idea it is possible to define multi-level programs with any number of levels. In most of the real life problems in mathematical programming, the parameters are considered as random variables. The branch of mathematical programming which deals with the theory and methods for the solution of conditional extremum problems under incomplete information about the random parameters is called “stochastic programming”. Supply chain planning problems are concerned with synchronizing and optimizing multiple activities involved in the enterprise, from the start of the process, such as procurement of the raw materials, through a series of process operations, to the end, such as distribution of the final product to customers. Enterprise-wide supply chain planning problems naturally exhibit a multi-level decision network structure, where for example, one level may correspond to a local plant control/scheduling/planning problem and another level to a corresponding plant-wide planning/network problem. Such a multi-level decision network structure can be mathematically represented by using “multi-level programming” principles. In this paper, we consider a “probabilistic bi-level linear multi-objective programming problem” and its application in enterprise-wide supply chain planning problem where (1) market demand, (2) production capacity of each plant and (3) resource available to all plants for each product are random variables and the constraints may consist of joint probability distributions or not. This probabilistic model is first converted into an equivalent deterministic model in each level, to which fuzzy programming technique is applied to solve the multi-objective nonlinear programming problem to obtain a compromise solution.

P.078 Pre-emptive analgesic efficacy of different analgesics after third molar surgery

The concept of measuring the membership degree along with a non-membership degree, gives rise to the intuitionistic fuzzy set theory. The present study formulates a nonlinear programming problem with multiple objectives, including all the parameters and decision variables as intuitionistic fuzzy numbers. The problem is further investigated subject to optimistic, pessimistic and mixed approaches under linear, exponential and hyperbolic membership functions. The article redefines pessimistic and mixed point of view to be in the true spirit compatible with the definition of an intuitionistic fuzzy number. Accuracy function is used to reduce the problem to an equivalent crisp multiobjective nonlinear programming problem and then optimal compromise solution is obtained under different approaches using various membership/non-membership functions. At appropriate places, theorems have also been proved to establish the equivalence between the original formulation and its crisp counterparts under each approach. Further, practical applications in production planning and transportation problem are illustrated to explain the optimistic, pessimistic and mixed approaches using the proposed algorithm and finally a comparison is also drawn.

Symmetric and self duality in vector optimization problem

In this paper, we present the first-order symmetric and self duality programs in multiobjective nonlinear programming problems. For these first-order symmetric and dual programs, we introduce the weak, strong, and converse duality theorems under convexity and concavity conditions, where the pair of the symmetric dual multiobjective nonlinear programming problems in this paper different which in [D.S. Kim, Y.B. Yun, H. Kuk, Second-order symmetric and self duality in multiobjective programming. Appl. Math. Lett. 10(2) (1997) 17–22]. Also, we prove the self duality theorem for these first-order self dual programs and illustrate its example.

Optimality Criteria and Duality in Multiobjective Programming Involving Nonsmooth Invex Functions

In this paper a generalization of invexity is considered in a general form, by means of the concept of K-directional derivative. Then in the case of nonlinear multiobjective programming problems where the functions involved are nondifferentiable, we established sufficient optimality conditions without any convexity assumption of the K-directional derivative. Then we obtained some duality results.

Multiobjective optimization and evolutionary algorithms for the application mapping problem in multiprocessor system-on-chip design

Sesame is a software framework that aims at developing a modeling and simulation environment for the efficient design space exploration of heterogeneous embedded systems. Since Sesame recognizes separate application and architecture models within a single system simulation, it needs an explicit mapping step to relate these models for cosimulation. The design tradeoffs during the mapping stage, namely, the processing time, power consumption, and architecture cost, are captured by a multiobjective nonlinear mixed integer program. This paper aims at investigating the performance of multiobjective evolutionary algorithms (MOEAs) on solving large instances of the mapping problem. With two comparative case studies, it is shown that MOEAs provide the designer with a highly accurate set of solutions in a reasonable amount of time. Additionally, analyses for different crossover types, mutation usage, and repair strategies for the purpose of constraints handling are carried out. Finally, a number of multiobjective optimization results are simulated for verification.

Interactive stability of multiobjective integer nonlinear programming problems

In this paper we present a solution method for fuzzy multiobjective integer nonlinear programming (FMOINLP) problems and the stability of this solution. An interactive stability compromise programming method for solving (FMOINLP) problems by using the compromise weights from the pay-off table of membership function for each objective function is presented.

A modified objective function method for solving nonlinear multiobjective fractional programming problems

A new method is used for solving nonlinear multiobjective fractional programming problems having V-invex objective and constraint functions with respect to the same function η. In this approach, an equivalent vector programming problem is constructed by a modification of the objective fractional function in the original nonlinear multiobjective fractional problem. Furthermore, a modified Lagrange function is introduced for a constructed vector optimization problem. By the help of the modified Lagrange function, saddle point results are presented for the original nonlinear fractional programming problem with several ratios. Finally, a Mond–Weir type dual is associated, and weak, strong and converse duality results are established by using the introduced method with a modified function. To obtain these duality results between the original multiobjective fractional programming problem and its original Mond–Weir duals, a modified Mond–Weir vector dual problem with a modified objective function is constructed.

Multiresponse robust design: Mean square error (MSE) criterion

Most of the published literature on robust design methodology is basically concerned with optimization of a single response or quality characteristic which is often most critical to consumers. However, manufactured products are typically characterized by numerous quality characteristics. In this paper we present a general framework for the multivariate problem when data are collected from a combined array. Within the framework, a mean square error (MSE) criterion is utilized and a non-linear multiobjective programming problem based on the individual MSE functions of each response is proposed for quality improvement. We adapted a suitable non-linear optimization algorithm to solve the proposed formulation. The optimization method used in this paper generates a string of solutions, called Pareto optimal solutions, rather than a “one shot” optimum solution to make selections and evaluate the trade-offs. The paper also presents an example and comparative results in order to demonstrate the potentials of the proposed approach.

Multiobjective second-order symmetric duality with F-convexity

We suggest a pair of second-order symmetric dual programs in multiobjective nonlinear programming. For these second-order symmetric dual programs, we prove the weak, strong and converse duality theorems under F-convexity conditions.

Sensitivity of Pareto Solutions in Multiobjective Optimization

The paper presents a sensitivity analysis of Pareto solutions on the basis of the Karush-Kuhn-Tucker (KKT) necessary conditions applied to nonlinear multiobjective programs (MOP) continuously depending on a parameter. Since the KKT conditions are of the first order, the sensitivity properties are considered in the first approximation. An analogue of the shadow prices, well known for scalar linear programs, is obtained for nonlinear MOPs. Two types of sensitivity are investigated: sensitivity in the state space (on the Pareto set) and sensitivity in the cost function space (on the balance set) for a vector cost function. The results obtained can be used in applications for sensitivity computation under small variations of parameters. Illustrative examples are presented.

An [formula omitted]-approximation method in nonlinear vector optimization

In this paper, a new approach for a solution of a nonlinear multiobjective programming problem is introduced. An equivalent η -approximated vector optimization problem is constructed by a modification of the objective and the constraint functions in the original multiobjective programming problem. The connection between (weak) efficient points in the original multiobjective programming problem and its equivalent η -approximated vector optimization problem is proved. In this way, optimality conditions for nonlinear constrained multiobjective programming problems having invex and/or generalized invex objective and constraint functions (with respect to the same functions η ) are obtained.

Sensitivity in multiobjective optimization: The generalized envolvent theorem

Sensitivity in the cost function space (on the balance set) is investigated, and generalized envolvent theorem is proved for nonlinear multiobjective programs. The results can be used in applications for computation of sensitivity under small variations of parameters.

Duality for Multi Objective Fractional Programming via Linearization and Scalarization Approaches

In this paper a dual is associated to a nonlinear multiobjective fractional programming problem using linearization and scalarization approaches involving differentiable functions. A multiobjective fractional dual in terms of Clarke’s generalized gradient is associated to the problem and certain duality results are established involving type I functions.

Second order symmetric duality for nonlinear multiobjective mixed integer programming

We formulate two pairs of second order symmetric duality for nonlinear multiobjective mixed integer programs for arbitrary cones. By using the concepts of efficiency and second order invexity, we establish weak, strong, converse and self-duality theorems for the dual models. Several known results are obtained as special cases.

Characterization of the stability set for non‐differentiable fuzzyparametric optimization problems

This note presents the characterization of the stability set of the first kind for multiobjective nonlinear programming (MONLP) problems with fuzzy parameters either in the constraints or in the objective functions without any differentiability assumptions. These fuzzy parameters are characterized by triangular fuzzy numbers (TFNs). The existing results concerning the parametric space in convex programs are reformulated to study for multiobjective nonlinear programs under the concept of α‐Pareto optimality.

An interactive fuzzy satisficing method based on the fractile optimization model using possibility and necessity measures for a fuzzy random multiobjective linear programming problem

This paper treats a multiobjective linear programming problem in which the coefficients contained in the objective function of the problem are fuzzy random variables. First, in order to take into account ambiguities of judgment by a human decision maker, fuzzy objectives are introduced. Subsequently, we consider a problem of maximizing the possibility and necessity of the objective function value to satisfy the fuzzy objectives. Since these degrees vary stochastically, a formulation is based on the fractile optimization model in a stochastic programming method. A process is presented for equivalent transformation to a deterministic multiobjective nonlinear fractional programming method. For the transformed multiobjective programming problem, an interactive fuzzy satisficing method that derives a satisfactory solution of the decision maker through interactions with the decision maker is proposed. It is shown that the global optimum solution of problems solved iteratively by an interactive process can be derived by means of an extended Dinkelbach-type algorithm. © 2005 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 88(5): 20–28, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.20136

Sufficiency and Duality in Multiobjective Programming with Generalized (F, ρ)-Convexity

A multiobjective nonlinear programming problem is considered. Sufficiency theorems are derived for efficient and properly efficient solutions under generalized (F, ρ)-convexity assumptions. Weak, strong and strict converse duality theorems are established for a general Mond–Weir type dual relating properly efficient solutions of the primal and dual problems.