An Improved Method for Solving Multiobjective Integer Linear Fractional Programming Problem

ABSTRACTIn this paper, we consider a Fuzzy Stochastic Linear Fractional Programming problem (FSLFP). In this problem, the coefficients and scalars in the objective function are the triangular fuzzy number and technological coefficients and the quantities on the right side of the constraints are fuzzy random variables with the specific distribution. Here we change an FSLFP problem to an equivalent deterministic Multi-objective Linear Fractional Programming (MOLFP) problem. Then by using Fuzzy Mathematical programming approach transformed MOLFP problem is reduced single objective Linear programming (LP) problem. A numerical example is presented to demonstrate the effectiveness of the proposed method.

The multi-objective linear fractional programming is an interesting topic with many applications in different fields. Until now, various algorithms have been proposed in order to solve the multi-objective linear fractional programming (MOLFP) problem. An important point in most of them is the use of non-linear programming with a high computational complexity or the use of linear programming with preferences of the objective functions which are assigned by the decision maker. The current paper, through combining goal programming and data envelopment analysis (DEA), proposes an iterative method to solve MOLFP problems using only linear programming. Moreover, the proposed method provides an efficient solution which fairly optimizes each objective function when the decision maker has no information about the preferences of the objective functions. In fact, along with normalization of the objective functions, their relative preferences are fairly determined using the DEA. The implementation of the proposed method is demonstrated using numerical examples.

This paper proposes an interactive fuzzy programming method for seeking a satisfactory solution for multiobjective two‐level linear fractional programming problems in which the decision makers in the upper and lower levels have several objectives, by first setting up the fuzzy goals for several objectives of each decision maker and seeking a satisfactory solution for the degree of satisfaction of two decision makers in a cooperative manner. In the proposed method, the decision maker at the upper level sets the minimal satisfactory levels for each fuzzy goal and the decision maker at the lower level determines the aspiration levels. The minimal satisfactory levels are treated as a constraint and the solution closest to the aspiration levels of the decision maker at the lower level is computed. The ratio of the aggregative degrees of satisfaction of the decision makers in the upper and lower levels with the obtained solution is evaluated by using partial information on the preference of the decision makers. If the ratio of the degrees of satisfaction satisfies the given condition and the decision maker at the upper level is satisfied with this solution, then the interaction is complete. Otherwise, a satisfactory solution is sought by updating the minimal satisfactory and aspiration levels. © 2003 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 86(9): 54–62, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.10047

Study on multi-objective linear fractional programming problem involving pentagonal intuitionistic fuzzy number

In this paper, we propose an alternate modified solution procedure based on fuzzy goal programming (FGP) approach for solving bi-level multiobjective linear fractional programming problems (BL-MOLFPPs) with some major modifications in the algorithm suggested by Abo-Sinna and Baky (2010). The proposed modified technique is simpler and requires less computational works than that of earlier technique. In the formulation of FGP models, each objective function (at both the levels - leader's level and follower's level) as well as control variables (of leader's level) are transformed into fuzzy goals with modified membership functions. Then FGP approach is used for achieving the highest degree of each of membership goals. To avoid decision deadlock situations and difficulties associated with bi-level programming problems, solution preferences by decision makers (at leader's and follower's levels) are not taken into account in proposed algorithm. A comparative analysis is also carried out with numerical example in order to show efficiency of proposed algorithm over earlier technique.

The authors deal with multiobjective linear programming problems with fuzzy random variable coefficients. Since the problem is ill-defined due to both fuzziness and randomness, we propose a decision making model based on E-model, which is a useful model in stochastic programming, and a possibility measure. First, we show that the formulated problem is reduced to a multiobjective linear fractional programming problem. After defining a Pareto optimal solution based on the expected value of possibility measure, we construct a solution algorithm for solving a minimax problem. Further, we consider interactive decision making using reference points and give numerical examples.

A multiobjective linear programming (MOLP) problem with fuzzy parameters in the constraints and objective functions in considered. Assuming fuzzy aspiration levels for the particular objectives and involving the comparison of fuzzy numbers, the problem is transformed into a multiobjective linear fractional program. For its solution an interactive method involving a linear programming procedure is proposed. This method has been developed for an application to long-term development planning of a water supply system. It has been first described in Slowinski (1986) and in this paper it is outlined and shown in the context of an updated review of recent proposals for multiobjective fuzzy linear programming.

programming problem (SOLFPP) and suggested an algorithm for its solution. The proposed method can be illustrated with the help of numerical example. The numerical result in this paper indicates that Advanced Harmonic Average technique is better than other techniques (such as Chandra Sen., Mean, Median, Geometric Mean, New Geometric Average and Harmonic average).

Finding the Efficient Solutions of Multiobjective Linear Fractional Programming Problems with Fuzzy Coefficients in Constraints and Vector of Resources

This paper studies the distribution network design problem considering the uncertain information in the demand, capacities, costs and prices in a multi-product environment and multiple periods. We consider a fractional objective function that consist in maximize the ratio between total profit and total cost. We use a model that integrates a facility location problem with a distribution network problem with fuzzy constraints, technological coefficients, and costs. To solve the problem, we use a method that transform the fuzzy linear fractional programming model in an equivalent multi-objective linear fractional programming problem to calculate the upper, middle and lower bounds of the original problem.

In this paper, a new method for solving multiobjective linear fractional programming problems with roughcoefficient (MORLFP) is proposed. The MORLFP problem is considered by incorporating rough intervals in thecoefficients of the objective functions. It is provided that a MORLFP problem is converted to an optimization problemwith rough interval valued objective functions which it their bounds are four multiobjective linear fractional functions.The rough efficient solutions are characterized by using a new proposed algorithm. A numerical example is given for thesake of illustration

This paper illustrates the use of goal attainment method with Taylor series approximation to produce a set of Pareto optimal solutions of a multi-objective linear fractional programming problem (MOLFPP). The relative gap in between the aspired goal and the feasible objective space is minimised along different search directions by introducing several non-negative and normalised weight vectors. To justify the effectiveness of our proposed method, a practical problem with numerical example is solved and the results are compared with that of existing fuzzy max-min operator method.

In this paper, an iterative technique based on the use of parametric functions is proposed to obtain the best preferred optimal solution of a multi-objective linear fractional programming problem. The decision maker ascertains own desired tolerance values for the objectives as termination constants and imposes them on each iteratively computed objective functions in terms of termination conditions. Each fractional objective is transformed into non-fractional parametric function using certain initial values of parameters. The parametric values are iteratively computed and $$\epsilon $$ -constraint method is used to obtain the pareto (weakly) optimal solutions in each step. The computations get terminated when all the termination conditions are satisfied at a pareto optimal solution of an iterative step. A numerical example is discussed at the end to illustrate the proposed method and fuzzy max–min operator method is applied to validate the obtained results.

In this paper, a method is developed to derive the acceptable ranges of objective values for a multi-objective linear fractional programming problem(MOLFPP) with fuzzy parameters both in objectives and constraints. \(\alpha \)- and \(\beta \)-cuts are respectively used in the objectives and constraints to specify the degrees of satisfaction and transform the fuzzy parameters into closed intervals. Using variable transformation and Taylor series expansion, the interval-valued fractional objectives are approximated by intervals of linear functions. The objective functions are assigned proper weights using analytic hierarchy process. Weighting sum method is used to transform the interval-valued multiple objectives into single objective. MOLFPP in interval-valued form is equivalently formulated as two linear problems which derive the acceptable ranges of objective values. Two numerical examples are illustrated to demonstrate the proposed method.

Problems modeled as a sum-of-ratios arise naturally when several rates (objectives) have to be optimized simultaneously. The linear sum-of-ratios problem is also used for computing nondominated solutions in multiobjective linear fractional programming problems when the weighted-sum is applied to the objective functions. We previously developed a Branch & Cut algorithm for computing solutions, considering a pre-defined error, for this kind of problems. The algorithm has a good performance for problems of medium dimensions (less than roughly ten ratios), even considering a very small pre-defined error. In this text we propose a combination of particle swarm optimization (PSO) techniques with the Branch & Cut algorithm in order to improve the performance of the computations for problems of higher dimensions. We present computational results for problems with up to twenty five ratios.KeywordsMultiobjective Linear Fractional Programming ProblemsMedium PropertiesSearch RegionElectromagnetism-like Mechanism (EM)Shift VectorThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.