This paper deals with the optimisation of various resources in a network under multiple objectives such as project duration and cost criteria. In this work, the Multiple electric Vehicles are considered to be the Mult...
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This paper deals with the optimisation of various resources in a network under multiple objectives such as project duration and cost criteria. In this work, the Multiple electric Vehicles are considered to be the Multiple Travelling Salesman Problem (mTSP). The main objective of this work is to reduce the overall travelling expenses and travelling space by the different Electric Vehicles. Hence with the condition that each one of the city is visited strictly once by one electric vehicle and the travelling dimensions keep on in definite boundaries. In this effort, fuzzy Multi Objective linearprogramming is formulated to solve the multiple TSP with single repository with numerical computations.
Solving fuzzylinearprogramming (FLP) requires the employment of a consistent ranking of fuzzy numbers. Ineffective fuzzy number ranking would lead to a flawed and erroneous solving approach. This paper presents a co...
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Solving fuzzylinearprogramming (FLP) requires the employment of a consistent ranking of fuzzy numbers. Ineffective fuzzy number ranking would lead to a flawed and erroneous solving approach. This paper presents a comprehensive and extensive review on fuzzy number ranking methods. Ranking techniques are categorised into six classes based on their characteristics. They include centroid methods, distance methods, area methods, lexicographical methods, methods based on decision maker's viewpoint, and methods based on left and right spreads. A survey on solving approaches to FLP is also reported. We then point out errors in several existing methods that are relevant to the ranking of fuzzy numbers and thence suggest an effective method to solve FLP. Consequently, FLP problems are converted into non-fuzzy single (or multiple) objective linearprogramming based on a consistent centroid-based ranking of fuzzy numbers. Solutions of FLP are then obtained by solving corresponding crisp single (or multiple) objective programming problems by conventional methods.
In this paper, a fuzzy multiobjective linear programming model is presented. Both the objective functions and the constraints are considered fuzzy. The coefficients of the decision variables in the objective functions...
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In this paper, a fuzzy multiobjective linear programming model is presented. Both the objective functions and the constraints are considered fuzzy. The coefficients of the decision variables in the objective functions and in the constraints, as well as the right-hand side of the constraints are assumed to be fuzzy numbers with either trapezoidal or triangular membership functions. The possibility programming approach is utilized to transform the fuzzy model to its crisp equivalent. A comparison between the global criterion method and the distance functions method, as two evaluation criteria, is illustrated by a computational study. (C) 2007 Elsevier Ltd. All rights reserved.
Neural Network(NN) is well-known as one of powerful computing tools to solve Optimization problems. Due to the massive computing unit-neurons and parallel mechanism of neural network approach we can solve the large-sc...
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Neural Network(NN) is well-known as one of powerful computing tools to solve Optimization problems. Due to the massive computing unit-neurons and parallel mechanism of neural network approach we can solve the large-scale problem efficiently and optimal solution can be gotten. In this paper, we intoroduce improvement of the two-phase approach for solving fuzzy multiobjectve linear progamming problem with both fuzzy objectives and constraints and we propose a new neural network technique for solving fuzzy multiobjective linear programming problems. The procedure and efficiency of this approach are shown with numerical simulations. (C) 1998 Elsevier Science Ltd. All rights reserved.
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