Fuzzy Quadratic Programming Problems Research Articles

Fuzzy modelling of static system optimum traffic assignment problem having multi origin-destination pair

Traffic congestion is an unpreventable problem to avoid in a transportation network and it has negative effects on traffic accident, time wasting, traffic delay and safety problem. Besides, in transportation networks, drivers do not want to deal with traffic jam while traversing between specified origin-destination pair. Therefore, traffic assignment (TA) is imperative to improve traffic management, transportation safety, time, and cost savings. System Optimum Traffic Assignment Problem (SOTAP) is a kind of TA model which aims to minimize the total system travel time on the network, and satisfies the flow conservation constraints. To model the SOTAP more realistically, the imprecise parameters can be taken as fuzzy. Therefore, in this paper, we focus on converting the conventional SOTAP to a fuzzy quadratic programming problem (QPP) which is named System Optimum Fuzzy Traffic Assignment Problem (SOFTAP). Here, link travel time is expressed with BPR function as generally used in the literature by converting to fuzzy except link-dependent parameters. Thus, the nonlinear objective function of SOFTAP is expressed in terms of fuzzy link flows and fuzzy link travel times. A solution approach from the literature is modified to the reconstructed SOFTAP.

System Optimum Fuzzy Traffic Assignment Problem

This paper focuses on converting the system optimum traffic assignment problem (SO-TAP) to system optimum fuzzy traffic assignment problem (SO-FTAP). The SO-TAP aims to minimize the total system travel time on road network between the specified origin and destination points. Link travel time is taken as a linear function of fuzzy link flow; thus each link travel time is constructed as a triangular fuzzy number. The objective function is expressed in terms of link flows and link travel times in a non-linear form while satisfying the flow conservation constraints. The parameters of the problem are path lengths, number of lanes, average speed of a vehicle, vehicle length, clearance, spacing, link capacity and free flow travel time. Considering a road network, the path lengths and number of lanes are taken as crisp numbers. The average speed of a vehicle and vehicle length are imprecise in nature, so these are taken as triangular fuzzy numbers. Since the remaining parameters, that are clearance, spacing, link capacity and free flow travel time are determined by the average speed of a vehicle and vehicle length, they will be triangular fuzzy numbers. Finally, the original SO-TAP is converted to a fuzzy quadratic programming (FQP) problem, and it is solved using an existing approach from literature. A numerical experiment is illustrated.

Fully Fuzzy Quadratic programming with unrestricted Fully Fuzzy variables and Parameters

There exist several methods for solving fuzzy linear or nonlinear programming problems under positivity fuzzy variables and restricted fuzzy coefficients. Due to the limitation of these methods, they can’t be applied for solving fully fuzzy linear or nonlinear programming problems with unrestricted fuzzy coefficients and fuzzy variables. In this paper an efficient method to find the fuzzy optimal solution for fully fuzzy quadratic programming (FFQP) problem with unrestricted variables and parameters has been proposed. All the coefficients and decision variables of both objective functions and the constraints are triangular fuzzy numbers. The proposed method is based on converted FFQP problem into crisp quadratic programming (CQP) problem. Finally an illustrative numerical example has been given to clarify the proposed solution method.

Solving fuzzy quadratic programming problems based on ABS algorithm

Recently, Ghanbari and Mahdavi-Amiri (Appl Math Model 34:3363–3375, 2010) gave the general compromised solution of an LR fuzzy linear system using ABS algorithm. Here, using this general solution, we solve quadratic programming problems with fuzzy LR variables. We convert fuzzy quadratic programming problem to a crisp quadratic problem by using general solution of fuzzy linear system. By using this method, the crisp optimization problem has fewer variables in comparison with other methods, specially when rank of the coefficient matrix is full. Thus, solving the fuzzy quadratic programming problem by using our proposed method is computationally easier than the solving fuzzy quadratic programming problem by using ranking function. Also, we study the fuzzy quadratic programming problem with symmetric variables. We show that, in this case, the associate quadratic programming problem is a convex problem, and thus, we able to find the global optimal.

A variables neighborhood search algorithm for solving fuzzy quadratic programming problems using modified Kerre’s method

To solve a fuzzy optimization problem, we need to compare fuzzy numbers. Here, we make use of our recently proposed modified Kerre’s method as an effective approach for comparison of LR fuzzy numbers. Using our new results on LR fuzzy numbers, we show that to compare two LR fuzzy numbers, we do not need to compute the fuzzy maximum of two numbers directly. We propose a new variable neighborhood search approach for solving fuzzy number quadratic programming problems by using the modified Kerre’s method. In our algorithm, a local search is performed using descent directions, found by solving five crisp mathematical programming problems. In several available methods, a fuzzy optimization problem is converted to a crisp problem, but in our proposed approach, using our modified Kerre’s method, the fuzzy optimization problem is solved directly, without changing it to a crisp program. We give some examples to compare the performance of our proposed algorithm with some available methods and show the effectiveness of our proposed algorithm by using the nonparametric statistical sign test.

Multi-objective fuzzy probabilistic quadratic programming problem

The aim of the paper is to present a multi-choice multi-objective fuzzy probabilistic quadratic programming problem and its solution methodology. The mathematical programming problem suggested here is difficult to solve directly. Therefore, three major steps are suggested to solve the proposed mathematical programming problem. In first step, fuzzy chance constraint is transformed to its equivalent chance constraint programming problem using α-cut technique. Chance constraint technique is used to obtain a crisp multi-choice multi-objective quadratic programming problem. In the next step, importance is given to handle multi-choice parameter using least square approximation technique. At the end of second step, a multi-objective quadratic mathematical programming is obtained. Finally, goal programming approach is used to solve the transformed multi-objective quadratic mathematical programming. Using existing methodology and software, the final solution of the proposed model is obtained. The proposed method is implemented with a numerical example.

A New Algorithm for Solving Fully Fuzzy Bi-Level Quadratic Programming Problems

A New Algorithm for Solving Fully Fuzzy Bi-Level Quadratic Programming Problems

Multi-objective fuzzy quadratic probabilistic programming problem involving fuzzy Cauchy random variable

Multi-objective fuzzy quadratic probabilistic programming problem involving fuzzy Cauchy random variable

Multi-objective fuzzy quadratic probabilistic programming problem involving fuzzy Cauchy random variable

In this paper, we have proposed a method for solving a multi-objective quadratic probabilistic programming problem, where the objective functions are multi-objective and quadratic in nature. The right hand side parameters are fuzzy Cauchy distributed independent random variable with location parameter δ and scale parameter β. The proposed mathematical programming problem is solved using two steps. First, the fuzziness is removed by using alpha cut technique and randomness is removed by chance constrained method. In second step, weighting method is used to solve the transformed multi-objective quadratic mathematical programming. This mathematical model is solved by existing methodology or software. A numerical example is presented to illustrate the efficiency and feasibility of the proposed method.

A Quadratic Programming with Triangular Fuzzy Numbers

Quadratic Programming (QP) is a mathematical modeling technique designed to optimize the usage of limited resources and has been widely applied to solve real world problems. In conventional quadratic programming model the parameters are known constants. However in many practical situations, it is not reasonable to require that the constraints or the objective function in quadratic programming problems be specified in precise, crisp terms. In such situations, it is desirable to use some type of Fuzzy Quadratic Programming (FQP) problem. In this paper a new approach is proposed to derive the fuzzy objective value of fuzzy quadratic programming problem, where the constraints coefficients and the right-hand sides are all triangular fuzzy numbers. The proposed method is solved using MATLABTM toolbox and the numerical results are presented.

THE PRINCIPAL PIVOTING METHOD FOR SOLVING FUZZY QUADRATIC PROGRAMMING PROBLEM

In this paper, a new approach for solving the Fuzzy Quadratic Pro- gramming Problem (FQPP) is suggested. Here, the cost coefficients, constraint coefficients and the right hand side coefficients are represent ed by triangular fuzzy numbers. Here the approach is on the basis of � - cut sets of fuzzy num- bers and the fuzzy number quadratic programming problems are reduced to interval number quadratic programming problem. The problem of quadratic programming with interval number coefficients is reduced to aFuzzy Linear Complementarity Problem (FLCP) using the KKT conditions and the principal pivoting method is proposed to solve the formulated model. The effectiveness of the proposed method is illustrated by means of an example.

Fuzzy costs in quadratic programming problems

Although quadratic programming problems are a special class of nonlinear programming, they can also be seen as general linear programming problems. These quadratic problems are of the utmost importance in an increasing variety of practical fields. As, in addition, ambiguity and vagueness are natural and ever-present in real-life situations requiring operative solutions, it makes perfect sense to address them using fuzzy concepts formulated as quadratic programming problems with uncertainty, i.e., as Fuzzy Quadratic Programming problems. This work proposes two novel fuzzy-sets-based methods to solve a particular class of Fuzzy Quadratic Programming problems which have vagueness coefficients in the objective function. Moreover, two other linear approaches are extended to solve the quadratic case. Finally, it is shown that the solutions reached from the extended approaches may be obtained from two proposed parametric multiobjective approaches.

Strict sensitivity analysis in fuzzy quadratic programming

Quadratic programming can be seen both as a general approach to linear programming and a special class of non-linear programming. Moreover, quadratic programming problems are of utmost importance in an increasing variety of practical fields, such as, regression, efficient production and portfolio selection. As ambiguity and vagueness are natural and ever-present in real-life situations requiring solutions, it makes perfect sense to attempt to address them using fuzzy quadratic programming problems. The main purpose of this paper is to study the strictly sensitivity analysis for fuzzy quadratic programming when simultaneously and independently variations occur in the right-hand-side of the constraints and the coefficients of the objective function. One presents computable auxiliary problems to identify the invariance intervals and give a fuzzy quadratic form of the optimal value function too. Some numerical examples are presented to illustrate the proposed method.

Extending and relating different approaches for solving fuzzy quadratic problems

Quadratic programming problems are applied in an increasing variety of practical fields. As ambiguity and vagueness are natural and ever-present in real-life situations requiring solutions, it makes perfect sense to attempt to address them using fuzzy quadratic programming problems. This work presents two methods used to solve linear problems with uncertainties in the set of constraints, which are extended in order to solve fuzzy quadratic programming problems. Also, a new quadratic parametric method is proposed and it is shown that this proposal contains all optimal solutions obtained by the extended approaches with their satisfaction levels. A few numerical examples are presented to illustrate the proposed method.

Duality in Fuzzy Quadratic Programming with Exponential Membership Functions

In this paper, we have presented fuzzy primal-dual quadratic programming problems and proved appropriate duality results taking exponential membership function.

On fuzzy random multiobjective quadratic programming

In this paper, a multiobjective quadratic programming problem having fuzzy random coefficients matrix in the objective and constraints and the decision vector are fuzzy pseudorandom variables is considered. First, we show that the efficient solutions of fuzzy quadratic multiobjective programming problems are resolved into series-optimal-solutions of relative scalar fuzzy quadratic programming. Some theorems are proved to find an optimal solution of the relative scalar quadratic multiobjective programming with fuzzy coefficients, having decision vectors as fuzzy variables. At the end, numerical examples are illustrated in the support of the obtained results.

A Survey of Fuzzy Quadratic Programming

Quadratic Programming (QP) represents a special class of nonlinear programming where the objective function is quadratic and constraints are linear. QP can also be viewed as a generalization of linear programming. When real-world applications are considered, vagueness appears in a natural way, and hence it makes perfect sense to think of fuzzy quadratic programming problems. This way of problem modeling is applied in an increasing variety of practical fields. In the first part of the paper, a general history and the approach of fuzzy linear mathematical programming are introduced. In the second part, the fuzzy quadratic mathematical programming is presented. Finally, some techniques and numerical examples using fuzzy quadratic mathematical programming are reviewed. Keywords: Fuzzy logic, decision making, convex set, fuzzy mathematical optimization, quadratic programming, portfolio selection problem

A revisit to quadratic programming with fuzzy parameters

Quadratic programming has been widely applied to solving real-world problems. Recently, Liu describes a solution method for solving a class of fuzzy quadratic programming problems, where the cost coefficients of the linear terms in objective function, constraint coefficients, and right-hand sides are fuzzy numbers [Liu ST. Quadratic programming with fuzzy parameters: a membership function approach. Chaos, Solitons & Fractals 2009;40:237–45]. In this paper, we generalize Liu’s method to a more general fuzzy quadratic programming problem, where the cost coefficients in objective function, constraint coefficients, and right-hand sides are all fuzzy numbers. A pair of two-level mathematical programs is formulated to calculate the upper bound and lower bound of the objective values of the fuzzy quadratic program. Based on the duality theorem and by applying the variable transformation technique, the pair of two-level mathematical programs is transformed into a family of conventional one-level quadratic programs. Solving the pair of quadratic programs produces the fuzzy objective values of the problem. With the ability of calculating the fuzzy objective value developed in this paper, it might help initiate wider applications.

Quadratic programming with fuzzy parameters: A membership function approach

Quadratic programming has been widely applied to solving real world problems. The conventional quadratic programming model requires the parameters to be known constants. In the real world, however, the parameters are seldom known exactly and have to be estimated. This paper discusses the fuzzy quadratic programming problems where the cost coefficients, constraint coefficients, and right-hand sides are represented by convex fuzzy numbers. Since the parameters in the program are fuzzy numbers, the derived objective value is a fuzzy number as well. Using Zadeh’s extension principle, a pair of two-level mathematical programs is formulated to calculate the upper bound and lower bound of the objective values of the fuzzy quadratic program. Based on the duality theorem and by applying the variable transformation technique, the pair of two-level mathematical programs is transformed into a family of conventional one-level quadratic programs. Solving the pair of quadratic programs produces the fuzzy objective values of the problem. An example illustrates method proposed in this paper.

On solutions of fuzzy random multiobjective quadratic programming with applications in portfolio problem

In this paper, a multiobjective quadratic programming problem fuzzy random coefficients matrix in the objectives and constraints and the decision vector are fuzzy variables is considered. First, we show that the efficient solutions fuzzy quadratic multiobjective programming problems series-optimal-solutions of relative scalar fuzzy quadratic programming. Some theorems are to find an optimal solution of the relative scalar quadratic multiobjective programming with fuzzy coefficients, having decision vectors as fuzzy variables. An application fuzzy portfolio optimization problem as a convex quadratic programming approach is discussed and an acceptable solution to such problem is given. At the end, numerical examples are illustrated in the support of the obtained results.