In many real-life decision making problems, probabilistic fuzzy goal programming problems are used where some of the input parameters of the problem are considered as random variables with fuzzy aspiration levels. In ...
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In many real-life decision making problems, probabilistic fuzzy goal programming problems are used where some of the input parameters of the problem are considered as random variables with fuzzy aspiration levels. In the present paper, a linearly constrained probabilistic fuzzy goal programmingprogramming problem is presented where the right hand side parameters in some constraints follows Pareto distribution with known mean and variance. Also the aspiration levels are considered as fuzzy. Further, simple, weighted, and preemptive additive approaches are discussed for probabilistic fuzzy goal programming model. These additive approaches are employed to aggregating the membership values and form crisp equivalent deterministic models. The resulting models are then solved by using standard linear mathematical programming techniques. The developed methodology and solution procedures are illustrated with a numerical example.
In this paper, we present a probabilistic fuzzy goal programming model to capture different uncertainties in an agricultural decision-making environment. First, we construct the goals of the model as probabilistic fuz...
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In this paper, we present a probabilistic fuzzy goal programming model to capture different uncertainties in an agricultural decision-making environment. First, we construct the goals of the model as probabilisticfuzzygoals. Next, we convert the probabilistic fuzzy goal programming problem to a probabilistic constrained programming problem. While a deterministic solution to this problem cannot be derived, we use a hybrid approach comprising Monte-Carlo simulation and a real-coded genetic algorithm. The application of the proposed model and the solution technique is demonstrated by considering the agricultural planning of the Danton-II community development block of Paschim Medinipur District, West Bengal, India. (C) 2015 Elsevier B.V. All rights reserved.
The post-disaster humanitarian logistic operations deal with the supply of emergency relief materials to mitigate damages in the affected areas. Immediately after the disaster, it is challenging to estimate the demand...
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The post-disaster humanitarian logistic operations deal with the supply of emergency relief materials to mitigate damages in the affected areas. Immediately after the disaster, it is challenging to estimate the demand for emergency relief materials. As a result, the demand for such materials at the point of demand and the corresponding transportation costs for the entire supply chain network becomes uncertain. This paper proposes a new probabilistic fuzzy goal programming model for making decisions to manage the post-disaster supply of emergency relief materials. A suggested procedure converts the proposed model to its deterministic equivalent when the demands for the relief materials follow uniform distributions. We implement the differential evolution, a metaheuristic technique, for analyzing demand satisfaction for relief materials under various scenarios. A case example based on the Nepal Earthquake in 2015 demonstrates the usefulness of the proposed approach. The solution of the model will help the Disaster Management Agency coordinate with the humanitarian organizations and foreign countries to provide the required emergency relief materials so that an adequate level of supply can be assured to the affected areas with the least possible transportation cost.
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