In this paper, a multi-item classical newsboy problem is formulated, occurring both fuzziness and randomness with floor space constraint. Here, the demand is considered as a random variable in fuzzy sense and also the...
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In this paper, a multi-item classical newsboy problem is formulated, occurring both fuzziness and randomness with floor space constraint. Here, the demand is considered as a random variable in fuzzy sense and also the purchase cost, salvage value, and selling price are fuzzy numbers. Initially, a method is explained for solving such a problem by using the minimization of a FN by Buckley [Buckley, JJ (2003). fuzzy probabilities: New approach and applications]. Then the fuzzy programming technique, e-constraint, and the weighted sum methods are applied to handle the multiobjective programming problem. The basic idea for transforming the fuzzy stochastic programming problem into a deterministic equivalent has been discussed. Finally, the deterministic model is solved by the LINGO package and is illustrated numerically.
This paper investigates a multi-objective capacitated solid transportation problem (MOCSTP) in an uncertain environment, where all the parameters are taken as zigzag uncertain variables. To deal with the uncertain MOC...
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This paper investigates a multi-objective capacitated solid transportation problem (MOCSTP) in an uncertain environment, where all the parameters are taken as zigzag uncertain variables. To deal with the uncertain MOCSTP model, the expected value model (EVM) and optimistic value model (OVM) are developed with the help of two different ranking criteria of uncertainty theory. Using the key fundamentals of uncertainty, these two models are transformed into their relevant deterministic forms which are further converted into a single-objective model using two solution approaches: minimizing distance method and fuzzy programming technique with linear membership function. Thereafter, the Lingo 18.0 optimization tool is used to solve the single-objective problem of both models to achieve the Pareto-optimal solution. Finally, numerical results are presented to demonstrate the application and algorithm of the models. To investigate the variation in the objective function, the sensitivity of the objective functions in the OVM model is also examined with respect to the confidence levels.
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