This paper is concerned with the solution methodology of a multi-objective transportation problem where fuzziness and randomness occur under one roof. In present problem, supplies demands are considered as fuzzy random variable. first step procedure, removed by using alpha-cut technique to obtain stochastic problem. By chance constrained technique, transformed equivalent crisp Then, introducing concept membership function, deterministic converted into single objective mathematical...

Abstract We consider a multi-objective linear programming problem where some of the right hand side parameters constraints are multi-choice in nature. For constraints, there may exist multiple choices, out which exactly one is to be chosen. The selection from sets should such manner that combination choices for each set provide best compromise solution. In order solve proposed problem, this paper proposes an equivalent mathematical model, can solved with help existing non-linear method....

Most of the real world decision making problems involve uncertainty, which arise due to incomplete information or linguistic on data. Stochastic programming and fuzzy are two powerful techniques solve such type problems.

Stochastic Programming is an art of modeling optimization problems in environment, where randomness occurs. In this manuscript, we present a multi-objective probabilistic programming problem, the random parameter follow logistic distribution. We transform model to equivalent deterministic mathematical by using chance constrained technique. Multiple number aspiration levels are allocated objective function Decision maker, main aim obtain such decision. After allocating several function, which...

The paper presents the solution methodology of a multi-objective probabilistic fractional programming problem, where parameters right hand side constraints follow Cauchy distribution. proposed mathematical model can not be solved directly. procedure is completed in three steps. In first step, problem converted to deterministic problem. second it its equivalent Finally, ε -constraint method applied find best compromise solution. A numerical example and application are presented demonstrate model.

Abstract This article develops a multi-choice multi-objective linear programming model in order to solve an integrated production planning problem of steel plant. The aim the is integrate sub-functions into single operation. are formulated by considering capacity different units plant, cost raw materials from various territories, demands customers geographical locations, time constraint for delivery products, and rate at stages process. Departure also considered formulation mathematical...

The multi-choice programming allows the decision maker to consider multiple number of resources for each constraint or goal. Multi-choice linear problem can not be solved directly using traditional technique. However, deal with parameters, multiplicative terms binary variables may used in transformed mathematical model. Recently, Biswal and Acharya (2009) have proposed a methodology transform an equivalent model, which accommodate maximum eight goals righthand side any constraint. In this...

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

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

This paper is concerned with the solution procedure of a multi-objective fuzzy stochastic optimisation problem by simulation-based genetic algorithm. In this article, chance constrained programming considered continuous random variables. The uncertain parameters are as normal and log-normal feasibilities constraints checked process without deriving deterministic equivalents. proposed illustrated numerical example.

AbstractIn this paper, we consider a fuzzy multi-choice linear programming problem where some of the parameters and decision variables are trapezoidal type numbers. In order to defuzzify general quantity concept nearest number is introduced. By assuming all as number, objective function left hand side constraints approximated their number. Interpolating polynomials formulated for parameters. Multi-choice replaced by with integer variables. Then an equivalent multi-objective non established....

This manuscript suggests a methodology to solve chance constraint multiple-objective linear fractional mathematical programming problem in which the parameters are dependent random variables each other. The proposed is formulated by taking few of as continuous variables. model cannot be solved directly using existing methodology. Thus order model, an equivalent deterministic derived. procedure accomplished two main steps. Initially, transformed help constrained method. In second step,...