A note of fuzzy programming and the pareto optimal set

We indicate some errors made in Buckley's paper on fuzzy programming and the Pareto optimal set [1].     

A language for nonlinear programming problems

An algebraic-like language for nonlinear programming problems is described. The rationale for the computation of the function values, gradients, and scond partial derivatives of the functions from their algebraic representation is developed. Each function is translated into an explicit factorable form or hierarchical representation which is used interpretively to compute the function value, gradient, and second partials of the function at each point for which such values are required. Computational efficiency is achieved by computing the matrix of second partials as the sum of a set of vector outer products, the vectors having resulted from the gradient computation, plus a diagonal matrix. An experimental computer program which implements the language and ties it to SUMT is described. In the experience with this program the computer times required have ranged from 4 to 30 times those times required by computer solutions to the same problems by using analyst-prepared programs to compute the function values, gradients, and second partial derivatives. A program based on a compiler approach to implementing the language, rather than the interpretative approach of the experimental program, will probably result in computer times between one and two times those required by using analyst-prepared programs.     

A method for solving a fuzzy linear programming

In this paper a fuzzy linear programming problem is presented. Then using the concept of comparison of fuzzy numbers, by the aid of the Mellin transform, we introduce a method for solving this problem.     

A reply to ‘a note on fuzzy programming and the pareto optimal set’

This note corrects the error in [1] as determined by [2].     

Dynamic programming for fuzzy systems with fuzzy environment

A dynamic programming functional equation for a multi-stage decision problem with fuzzy dynamics and environment is formulated and solved by a process of fuzzy interpolation. This is an extension of the Bellman-Zadeh model.     

A new approach to fuzzy programming

A fuzzy program is defined in the usual way as a sequence of statements (instruction) which are considered as functions (possibly fuzzy functions) and fuzzy predicates defined on the given input domain. The essential difference in the approach presented in this paper is the new interpretation of the execution of fuzzy programs, and a new method of evaluating fuzzy predicates. The result of the fuzzy program execution is an appropriate fuzzy subset in the output domain.     

A structure-conveying modelling language for mathematical and stochastic programming

We present a structure-conveying algebraic modelling language for mathematical programming. The proposed language extends AMPL with object-oriented features that allows the user to construct models from sub-models, and is implemented as a combination of pre- and post-processing phases for AMPL. Unlike traditional modelling languages, the new approach does not scramble the block structure of the problem, and thus it enables the passing of this structure on to the solver. Interior point solvers that exploit block linear algebra and decomposition-based solvers can therefore directly take advantage of the problem’s structure. The language contains features to conveniently model stochastic programming problems, although it is designed with a much broader application spectrum.     

A geometric approach for solving fuzzy linear programming problems

In this paper we first recall some definitions and results of fuzzy plane geometry, and then introduce some definitions in the geometry of two-dimensional fuzzy linear programming (FLP). After defining the optimal solution based on these definitions, we use the geometric approach for obtaining optimal solution(s) and show that the algebraic solutions obtained by Zimmermann method (ZM) and our geometric solutions are the same. Finally, numerical examples are solved by these two methods.     

Interactive fuzzy goal programming approach for bilevel programming problem

This paper presents an interactive fuzzy goal programming (FGP) approach for bilevel programming problems with the characteristics of dynamic programming (DP).     

A unified treatment of fuzzy set theory and Boolean-valued set theory—Fuzzy set structures and normal fuzzy set structures

This is Part I of a two-part paper; the purpose of this two-part paper is (a) to develop new concepts and techniques in the theory of infinite-dimensional programming, and (b) to obtain fruitful applications in continuous time programming. Part I deals with the development of continuous time analogues to those concepts which are the cornerstones of finite-dimensional programming theory. Specifically, a constraint qualification analogous to that found in finite-dimensional programming and a continuous time version of Farkas' theorem are developed. The latter result, stated in terms of convex and polar cones, is then employed in conjunction with the newly-developed constraint qualification to establish necessary conditions and a duality theory for a class of nonlinear continuous time programming problems. This approach to duality permits the imposition of assumptions that are less stringent than those needed for duality in previous formulations of the nonlinear problem.     

A new method for solving fully fuzzy linear programming problems

Lotfi et al. [Solving a full fuzzy linear programming using lexicography method and fuzzy approximate solution, Appl. Math. Modell. 33 (2009) 3151–3156] pointed out that there is no method in literature for finding the fuzzy optimal solution of fully fuzzy linear programming (FFLP) problems and proposed a new method to find the fuzzy optimal solution of FFLP problems with equality constraints. In this paper, a new method is proposed to find the fuzzy optimal solution of same type of fuzzy linear programming problems. It is easy to apply the proposed method compare to the existing method for solving the FFLP problems with equality constraints occurring in real life situations. To illustrate the proposed method numerical examples are solved and the obtained results are discussed.     

A new solution method for fuzzy chance constrained programming problem

This paper deals with an optimization model, where both fuzziness and randomness occur under one roof. The concept of fuzzy random variable (FRV), mean and variance of FRV is used in the model. In particular, the methodology is developed in the presence of FRV in the constraint. The methodology is verified through numerical examples.     

A fuzzy programming approach for dynamic virtual hub location problem

Hub location problem has been used in transportation network to exploit economies of scale. For example, a controversial issue in the planning of air transportation networks is inclement weather or emergency conditions. In this situation, hub facilities would not be able to provide a good service to their spoke nodes temporarily. Thus, some other kinds of predetermined underutilized facilities in the network are used as virtual hubs to host some or all connections of original hubs to recover the incurred incapacitation and increase network flexibility and demand flow. In such an unexpected situation, it is not unreasonable to expect that some information be imprecise or vague. To deal with this issue, fuzzy concept is used to pose a more realistic problem. Here, we present a fuzzy integer liner programming approach to propose a dynamic virtual hub location problem with the aim of minimizing transportation cost in the network. We examine the effectiveness of our model using the well-known CAB data set.     

On a programming language for graph algorithms

An algorithmic language, GRAAL, is defined, as an extension of ALGOL 60 (Revised), for describing and implementing graph algorithms of the type arising in applications. It is based on a set algebraic model of graph theory which defines the graph structure in terms of user specified morphisms between certain set algebraic structures over the node and arc set. Several examples of graph algorithms written in GRAAL are included.This work was in part supported by Grant GJ-1067 from the U.S. National Science Foundation and Grant NGL-21-002-008 from the U.S. National Aeronautics and Space Administration.     

A set of staircase linear programming test problems

A set of staircase linear programming test problems are made available for computational experiments.Research supported by the U.S. Department of Energy under contract DE-AC02-76CH00016.Research supported by the Belgian National Research Program in Energy, contract E/I/3.     

On fuzzy linear programming

Fuzzy multi-objective and fuzzy Goal Programming are discussed in connection with several membership functions which are used to transform the original problem into three equivalent linear programming problems. Existence and uniqueness theorems are given. Fuzzy duality is presented, and an extension of the initial fuzzy problem arises immediately from it.