On the unique solvability of fuzzy relational equations

The solution set of a consistent system of fuzzy relational equations with max-min composition can be characterized by one maximum solution and a finite number of minimal solutions. A polynomial-time method of O(mn) complexity is proposed to determine whether such a system has a unique minimal solution and/or a unique solution, where m, n are the dimensions of the input data. The proposed method can be extended to examining a system of fuzzy relational equations with max-T composition where T is a continuous triangular norm.     

A note on solution sets of interval-valued fuzzy relational equations

This note discusses three types of solutions for a system of interval-valued fuzzy relational equations with max-T composition and illustrates their relations to the solutions of a system of fuzzy relational inequalities with max-T composition. It validates the major claims appeared in Fuzzy Optimization and Decision Making, 2 (2003) 41–60; 4 (2005) 331–349.     

多变量FUZZY关系方程组解的进一步研究

本文给出一个把多变量ＦＵＺＺＹ关系方程组的求解问题转化为单变量ＦＵＺＺＹ关系方程组来加以解决的方法，这一方法被应用到关于解集结构的研究上，获得了良好的结果。     

On the resolution and optimization of a system of fuzzy relational equations with sup-T composition

This paper provides a thorough investigation on the resolution of a finite system of fuzzy relational equations with sup-T composition, where T is a continuous triangular norm. When such a system is consistent, although we know that the solution set can be characterized by a maximum solution and finitely many minimal solutions, it is still a challenging task to find all minimal solutions in an efficient manner. Using the representation theorem of continuous triangular norms, we show that the systems of sup-T equations can be divided into two categories depending on the involved triangular norm. When the triangular norm is Archimedean, the minimal solutions correspond one-to-one to the irredundant coverings of a set covering problem. When it is non-Archimedean, they only correspond to a subset of constrained irredundant coverings of a set covering problem. We then show that the problem of minimizing a linear objective function subject to a system of sup-T equations can be reduced into a 0–1 integer programming problem in polynomial time. This work generalizes most, if not all, known results and provides a unified framework to deal with the problem of resolution and optimization of a system of sup-T equations. Further generalizations and related issues are also included for discussion.     

A survey on fuzzy relational equations, part I: classification and solvability

Fuzzy relational equations play an important role in fuzzy set theory and fuzzy logic systems, from both of the theoretical and practical viewpoints. The notion of fuzzy relational equations is associated with the concept of “composition of binary relations.” In this survey paper, fuzzy relational equations are studied in a general lattice-theoretic framework and classified into two basic categories according to the duality between the involved composite operations. Necessary and sufficient conditions for the solvability of fuzzy relational equations are discussed and solution sets are characterized by means of a root or crown system under some specific assumptions.     

一类基于有界算子合成的模糊关系方程

讨论模糊关系的有界和 -有界积合成的基本性质。对于论域 U上的一个自反和有界传递的模糊关系 R,证明它是一个预序关系。得到关于有界算子的模糊线性方程有解的充要条件及解的递归结构。在此基础上给出有限论域上的模糊关系方程 A·X=B的求解方法     

Min–max programming problem with constraints of addition-min-product fuzzy relation inequalities

Fuzzy Optimization and Decision Making - In this paper, we study a new type of fuzzy relation system called fuzzy relational inequalities with addition-min-product composition operations to model a...     

Solution Sets of Interval-Valued Min-S-Norm Fuzzy Relational Equations

This paper introduces the concepts of tolerable solution set, united solution set, and controllable solution set for interval-valued fuzzy relational equations. Given a continuous s-norm, it is shown that each of the three types of the solution sets of interval-valued fuzzy relational equations with a min-s-norm composition, if nonempty, is composed of one minimum solution and a finite number of maximal solutions. Necessary and sufficient conditions for the existence of solutions are given. Computational procedures based on the constructive proofs are proposed to generate the complete solution sets. An example is given to illustrate the proposed procedures. An erratum to this article is available at .     

An two phase abs method for solving over determined systems of linear inequalities

The method, called the Multi-Stage ABS algorithm, for solving the over-determined linear inequalities system and the system combined with the over-determined linear inequalities and the equations is presented. This method is characterized by translating inequalities system to an equations system with slack variables. The explicit solution with the slack variables of the equations system are given by the implicit LU algorithm, then the slack variables can be given by the ABS algorithm. Finally, the upper multiplications of the algorithm are given.     

Solution Sets of Interval-Valued Fuzzy Relational Equations

This paper introduces the concepts of tolerable solution set, united solution set, and controllable solution set of interval-valued fuzzy relational equations. Given a continuous t-norm, it is proved that each of the three types of the solution sets of interval-valued fuzzy relational equations with a max-t-norm composition, if nonempty, is composed of one maximum solution and a finite number of minimal solutions. Necessary and sufficient conditions for the existence of solutions are given. Computational procedures based on the constructive proofs are proposed to generate the complete solution sets. Examples are given to illustrate the procedures. An erratum to this article is available at .     

Minimizing a Linear Objective Function with Fuzzy Relation Equation Constraints

An minimization problem with a linear objective function subject to fuzzy relation equations using max-product composition has been considered by Loetamonphong and Fang. They first reduced the problem by exploring the special structure of the problem and then proposed a branch-and-bound method to solve this 0-1 integer programming problem. In this paper, we provide a necessary condition for an optimal solution of the minimization problems in terms of one maximum solution derived from the fuzzy relation equations. This necessary condition enables us to derive efficient procedures for solving such optimization problems. Numerical examples are provided to illustrate our procedures.     

Geometric programming problem with single-term exponents subject to max-product fuzzy relational equations

In this paper, an optimization model for minimizing an objective function with single-term exponents subject to fuzzy relational equations specified in max-product composition is presented. The solution set of such a fuzzy relational equation is a non-convex set. First, we present some properties for the optimization problem under the assumptions of both negative and nonnegative exponents in the objective function. Second, an efficient procedure is developed to find an optimal solution without looking for all the potential minimal solutions and without using the value matrix. An example is provided to illustrate the procedure.     

Randomly generating test problems for fuzzy relational equations

Fuzzy relational equations play an important role in fuzzy set theory and fuzzy logic systems. To compare and evaluate the accuracy and efficiency of various solution methods proposed for solving systems of fuzzy relational equations as well as the associated optimization problems, a test problem random generator for systems of fuzzy relational equations is needed. In this paper, procedures for generating test problems of fuzzy relational equations with the sup-T{\mathcal{T}} composition are proposed for the cases of sup-T_M{\mathcal{T}_M}, sup-T_P{\mathcal{T}_P}, and sup-T_L{\mathcal{T}_L } compositions. It is shown that the test problems generated by the proposed procedures are consistent. Some properties are discussed to show that the proposed procedures randomly generate systems of fuzzy relational equations with various number of minimal solutions. Numerical examples are included to illustrate the proposed procedures.     

A Note on Fuzzy Relation Programming Problems with Max-Strict-t-Norm Composition

The fuzzy relation programming problem is a minimization problem with a linear objective function subject to fuzzy relation equations using certain algebraic compositions. Previously, Guu and Wu considered a fuzzy relation programming problem with max-product composition and provided a necessary condition for an optimal solution in terms of the maximum solution derived from the fuzzy relation equations. To be more precise, for an optimal solution, each of its components is either 0 or the corresponding component's value of the maximum solution. In this paper, we extend this useful property for fuzzy relation programming problem with max-strict-t-norm composition and present it as a supplemental note of our previous work.     

ABS methods for continuous and integer linear equations and optimization

ABS methods are a large class of algorithms for solving continuous and integer linear algebraic equations, and nonlinear continuous algebraic equations, with applications to optimization. Recent work by Chinese researchers led by Zunquan Xia has extended these methods also to stochastic, fuzzy and infinite systems, extensions not considered here. The work on ABS methods began almost thirty years. It involved an international collaboration of mathematicians especially from Hungary, England, China and Iran, coordinated by the university of Bergamo. The ABS method are based on the rank reducing matrix update due to Egerváry and can be considered as the most fruitful extension of such technique. They have led to unification of classes of methods for several problems. Moreover they have produced some special algorithms with better complexity than the standard methods. For the linear integer case they have provided the most general polynomial time class of algorithms so far known; such algorithms have been extended to other integer problems, as linear inequalities and LP problems, in over a dozen papers written by Iranian mathematicians led by Nezam Mahdavi-Amiri. ABS methods can be implemented generally in a stable way, techniques existing to enhance their accuracy. Extensive numerical experiments have shown that they can outperform standard methods in several problems. Here we provide a review of their main properties, for linear systems and optimization. We also give the results of numerical experiments on some linear systems. This paper is dedicated to Professor Egerváry, developer of the rank reducing matrix update, that led to ABS methods.     

A two-stage fuzzy robust integer programming approach for capacity planning of environmental management systems

In this study, a two-stage fuzzy robust integer programming (TFRIP) method has been developed for planning environmental management systems under uncertainty. This approach integrates techniques of robust programming and two-stage stochastic programming within a mixed integer linear programming framework. It can facilitate dynamic analysis of capacity-expansion planning for waste management facilities within a multi-stage context. In the modeling formulation, uncertainties can be presented in terms of both possibilistic and probabilistic distributions, such that robustness of the optimization process could be enhanced. In its solution process, the fuzzy decision space is delimited into a more robust one by specifying the uncertainties through dimensional enlargement of the original fuzzy constraints. The TFRIP method is applied to a case study of long-term waste-management planning under uncertainty. The generated solutions for continuous and binary variables can provide desired waste-flow-allocation and capacity-expansion plans with a minimized system cost and a maximized system feasibility.     

Nonnegative integral solution of linear equations

A method to obtain a nonnegative integral solution of a system of linear equations, if such a solution exists is given. The method writes linear equations as an integer programming problem and then solves the problem using a combination of artificial basis technique and a method of integer forms.     

The solution sets of infinite fuzzy relational equations with sup-conjunctor composition on complete distributive lattices

This paper deals with sup-conjunctor composition fuzzy relational equations in infinite domains and on complete distributive lattices. When its right-hand side is a continuous join-irreducible element or has an irredundant continuous join-decomposition, a necessary and sufficient condition describing an attainable solution (resp. an unattainable solution) is formulated and some properties of the attainable solution (resp. the unattainable solution) are shown. Further, the structure of solution sets is investigated.     

Studying the stability of solutions to systems of linear inequalities and constructing separating hyperplanes

We consider the methods for matrix correction or correction of all parameters of systems of linear equations and inequalities. We show that the problem of matrix correction of an inconsistent system of linear inequalities with the nonnegativity condition is reduced to a linear programming problem. Some stability measure is defined for a given solution to a system of linear inequalities as the minimal possible variation of parameters under which this solution does not satisfy the system. The problem of finding the most stable solution to the system is considered. The results are applied to constructing an optimal separating hyperplane in the feature space that is the most stable to the changes of features of the objects.