Fuzzy正项几何规划的直接算法

将Fuzzy正项几何规划化的一变量有上、下界限制的Fuzzy正项几何规划,利用Fuzzy几何不等式,又将该变量有上、下界限制的Fuzzy正项几何规划化为单项Fuzzy正项几何规划,提出基于Fuzzy值集割平面法的两种直接算法,并通过一个数值例证实该方法的有效性。     

New method to posynomial geometric programming of trapezoidal fuzzy numbers

This paper presents a method for solving posynomial geometric programming with fuzzy coefficients. By utilizing comparison of fuzzy numbers with a method, the programming with fuzzy coefficients is reduced to the programming with constant coefficients. Then the programming with fuzzy coefficients can be solved by using a method for posynomial geometric programming. Finally, one comparative example is used to illustrate advantage of the new method.     

The complementary unboundedness of dual feasible solution sets in convex programming

F.E. Clark has shown that if at least one of the feasible solution sets for a pair of dual linear programming problems is nonempty then at least one of them is both nonempty and unbounded. Subsequently, M. Avriel and A.C. Williams have obtained the same result in the more general context of (prototype posynomial) geometric programming. In this paper we show that the same result is actually false in the even more general context of convex programming — unless a certain regularity condition is satisfied.We also show that the regularity condition is so weak that it is automatically satisfied in linear programming (prototype posynomial) geometric programming, quadratic programming (with either linear or quadratic constraints),l _p -regression analysis, optimal location, roadway network analysis, and chemical equilibrium analysis. Moreover, we develop an equivalent regularity condition for each of the usual formulations of duality.Research sponsored by the Air Force Office of Scientific Research, Air Force Systems Command, USAF, under Grant Number AFOSR-73-2516.     

A General Alternative Procedure for Solving Negative Degree of Difficulty Problems in Geometric Programming

The degree of difficulty is an important concept in classical geometric programming theory. The dual problem is often infeasible when the degree of difficulty is negative and little has been published on this topic. In this paper, an alternative procedure is developed to find the optimal solution for the posynomial geometric programming problem with a negative degree of difficulty. First an equivalent problem was constructed with a positive degree of difficulty and the general posynomial geometric programming problem was solved using an original method previously developed by the authors. This method avoids the difficulty of non-differentiability of the dual objective function in the classical methods classified as dual. It also avoids the problem that appears when the feasible region for the dual problem is formed by an inconsistent system of linear equations.     

Inventory model of deteriorated items with a constraint: A geometric programming approach

An inventory model for deteriorating items is built-up with limited storage space. Here, demand rate for the items is finite, items deteriorate at constant rates and are replenished instantaneously. Following EOQ model, the problem is formulated with and without truncation on the deterioration term and ultimately is converted to the minimization of a signomial expression with a posynomial constraint. It is solved by modified geometric programming (MGP) method and non-linear programming (NLP) method. The problem is supported by numerical examples. The results from two versions of the model (with and without truncation) and two methods (i.e. MGP and NLP) are compared.     

A modified goal programming model for piecewise linear functions

Piecewise linear function (PLF) is an important technique for solving polynomial and/or posynomial programming problems since the problems can be approximately represented by the PLF. The PLF can also be solved using the goal programming (GP) technique by adding appropriate linearization constraints. This paper proposes a modified GP technique to solve PLF with n terms. The proposed method requires only one additional constraint, which is more efficient than some well-known methods such as those proposed by Charnes and Cooper's, and Li. Furthermore, the proposed model (PM) can easily be applied to general polynomial and/or posynomial programming problems.     

Optimal value bounds in nonlinear programming with interval data

We consider nonlinear programming problems the input data of which are not fixed, but vary in some real compact intervals. The aim of this paper is to determine bounds of the optimal values. We propose a general framework for solving such problems. Under some assumption, the exact lower and upper bounds are computable by using two non-interval optimization problems. While these two optimization problems are hard to solve in general, we show that for some particular subclasses they can be reduced to easy problems. Subclasses that are considered are convex quadratic programming and posynomial geometric programming.     

A new algorithm for geometric programming based on the linear structure of its dual problem

In this article, we present an algorithm for the resolution of a nonlinear optimization problem, concretely the posynomial geometric programming model. The solution procedure that we develop extends the condensation techniques for geometric programming, allowing us to find the optimal solutions to the dual geometric problems that we get from the interior of the corresponding feasible regions, in the line that interior point methods for linear programming work, which leads us to obtain considerable computational advantages with respect of the classical solution procedures.     

Posynomial geometric programming as a special case of semi-infinite linear programming

This paper develops a wholly linear formulation of the posynomial geometric programming problem. It is shown that the primal geometric programming problem is equivalent to a semi-infinite linear program, and the dual problem is equivalent to a generalized linear program. Furthermore, the duality results that are available for the traditionally defined primal-dual pair are readily obtained from the duality theory for semi-infinite linear programs. It is also shown that two efficient algorithms (one primal based and the other dual based) for geometric programming actually operate on the semi-infinite linear program and its dual.     

Geometric programming with signomials

The difference of twoposynomials (namely, polynomials with arbitrary real exponents, but positive coefficients and positive independent variables) is termed asignomial.Each signomial program (in which a signomial is to be either minimized or maximized subject to signomial constraints) is transformed into an equivalent posynomial program in which a posynomial is to be minimized subject only to inequality posynomial constraints. The resulting class of posynomial programs is substantially larger than the class of (prototype)geometric programs (namely, posynomial programs in which a posynomial is to be minimized subject only to upper-bound inequality posynomial constraints). However, much of the (prototype) geometric programming theory is generalized by studying theequilibrium solutions to thereversed geometric programs in this larger class. Actually, some of this theory is new even when specialized to the class of prototype geometric programs. On the other hand, all of it can indirectly, but easily, be applied to the much larger class of well-posedalgebraic programs (namely, programs involving real-valued functions that are generated solely by addition, subtraction, multiplication, division, and the extraction of roots).This research was partially supported by Research Grant No. DA-AROD-31-124-6680 from the Army Research Office, Durham, North Carolina, and by a Summer Fellowship Grant from Northwestern University.     

Fenchel-Lagrange Duality Versus Geometric Duality in Convex Optimization

We present a new duality theory to treat convex optimization problems and we prove that the geometric duality used by Scott and Jefferson in different papers during the last quarter of century is a special case of it. Moreover, weaker sufficient conditions to achieve strong duality are considered and optimality conditions are derived. Next, we apply our approach to some problems considered by Scott and Jefferson, determining their duals. We give weaker sufficient conditions to achieve strong duality and the corresponding optimality conditions. Finally, posynomial geometric programming is viewed also as a particular case of the duality approach that we present. Communicated by V. F. Demyanov The first author was supported in part by Gottlieb Daimler and Karl Benz Stiftung 02-48/99. The second author was supported in part by Karl und Ruth Mayer Stiftung.     

A rough set approach to intuitionistic fuzzy soft set based decision making

The soft set theory, originally proposed by Molodtsov, can be used as a general mathematical tool for dealing with uncertainty. Since its appearance, there has been some progress concerning practical applications of soft set theory, especially the use of soft sets in decision making. The intuitionistic fuzzy soft set is a combination of an intuitionistic fuzzy set and a soft set. The rough set theory is a powerful tool for dealing with uncertainty, granuality and incompleteness of knowledge in information systems. Using rough set theory, this paper proposes a novel approach to intuitionistic fuzzy soft set based decision making problems. Firstly, by employing an intuitionistic fuzzy relation and a threshold value pair, we define a new rough set model and examine some fundamental properties of this rough set model. Then the concepts of approximate precision and rough degree are given and some basic properties are discussed. Furthermore, we investigate the relationship between intuitionistic fuzzy soft sets and intuitionistic fuzzy relations and present a rough set approach to intuitionistic fuzzy soft set based decision making. Finally, an illustrative example is employed to show the validity of this rough set approach in intuitionistic fuzzy soft set based decision making problems.     

A computational study of methods for solving polynomial geometric programs

A computational comparison of several methods for dealing with polynomial geometric programs is presented. Specifically, we compare the complementary programs of Avriel and Williams (Ref. 1) with the reversed programs and the harmonic programs of Duffin and Peterson (Refs. 2, 3). These methods are used to generate a sequence of posynomial geometric programs which are solved using a dual algorithm.The authors would like to acknowledge the helpful comments of the referees. Also, they would like to acknowledge the programming assistance of Mr. S. N. Wong of The Pennsylvania State University. The first author's research was supported in part by a Research Initiation Grant awarded through The Pennsylvania State University.     

Covering rough sets based on neighborhoods: An approach without using neighborhoods

Rough set theory, a mathematical tool to deal with inexact or uncertain knowledge in information systems, has originally described the indiscernibility of elements by equivalence relations. Covering rough sets are a natural extension of classical rough sets by relaxing the partitions arising from equivalence relations to coverings. Recently, some topological concepts such as neighborhood have been applied to covering rough sets. In this paper, we further investigate the covering rough sets based on neighborhoods by approximation operations. We show that the upper approximation based on neighborhoods can be defined equivalently without using neighborhoods. To analyze the coverings themselves, we introduce unary and composition operations on coverings. A notion of homomorphism is provided to relate two covering approximation spaces. We also examine the properties of approximations preserved by the operations and homomorphisms, respectively.     

Fuzzy正项几何规划的分类及相应类的性质

本文将Ｆｕｚｚｙ正项几何规划分成三类，并分别探讨了其相容性与Ｆｕｚｚｙ最优解问题。     

模糊粗糙集及粗糙模糊集的模糊度

1965年,Zadeh提出了Fuzzy集理论,1982年,Z.Pawlak提出Rough集理论。将二者结合而形成的模糊粗糙集（FR集）及粗糙模糊集（RF集）近年来越来越受到国际学术界的关注。本文所研究的FR集及RF集的模糊度,是对FR集及RF集模糊程度的一种度量,进而引进了相应的明可夫斯基距离,明可夫斯基模糊度和Shannon模糊度。     

一般的正项几何规划的一种分解方法

关于几何规划的分解方法，［１３］均给出特殊类型的几何规划的分解方法，本文则对一般的正项几何规划给出一种直接分解方法。     

单向S-粗集粗糙性的度量方法

根据单向S-粗集内(外)边界的定义,引入了外边界熵的概念,将外边界熵与知识粒度结合进来,提出了一种新的单向S-粗集粗糙性的度量方法,讨论了这一度量的特性。通过分析和实例可以看出,这一新的度量方法可以用来更合理、更精确地度量单向S-粗集的不确定性。     

模糊粗糙集的表示及应用

一个模糊粗糙集是一对模糊集,它可以用一簇经典粗糙集表示出来.本文研究了模糊粗糙集的表示问题,利用模糊集的分解定理证明了一个模糊粗糙集可以用一簇粗糙模糊集表示出来,利用这个结果可以证明模糊粗糙集的一些重要性质.     

Sensitivity analysis in posynomial geometric programming

Sensitivity analysis results for general parametric posynomial geometric programs are obtained by utilizing recent results from nonlinear programming. Duality theory of geometric programming is exploited to relate the sensitivity results derived for primal and dual geometric programs. The computational aspects of sensitivity calculations are also considered.This work was part of the doctoral dissertation completed in the Department of Operations Research, George Washington University, Washington, DC. The author would like to express his gratitude to the thesis advisor, Prof. A. V. Fiacco, for overall guidance and stimulating discussions which inspired the development of this research work.